Primary Surplus Behavior and Risks to Fiscal Sustainability in Emerging Market Countries: A "Fan-Chart" Approach

This paper proposes a probabilistic approach to public debt sustainability analy-sis (DSA) using "fan charts." These depict the magnitude of risks-upside and downside-surrounding public debt projections as a result of uncertain economic conditions and policies. We propose a simulation algorithm for the path of public debt under realistic shock configurations, combining pure economic disturbances (to growth, interest rates, and exchange rates), the endogenous policy response to these, and the possible shocks arising from fiscal policy itself. The paper empha-sizes the role of fiscal behavior, as well as the structure of disturbances facing the economy and due to fiscal policy, in shaping the risk profile of public debt. Fan charts for debt are derived from the "marriage" between the pattern of shocks on the one hand and the endogenous response of fiscal policy on the other. Applications to Argentina, Brazil, Mexico, South Africa, and Turkey are used to illustrate the approach and its limitations. Copyright 2006, International Monetary Fund

What is new in the prevention of ventilator-associated pneumonia?

Purpose of review: Ventilator-associated pneumonia (VAP) remains a frequent and severe complication in endotracheally intubated patients. Strict adherence to preventive measures reduces the risk of VAP. The objective of this paper is to review what has come forward in recent years in the nonpharmacological prevention of VAP. Recent findings: It seems advantageous to implement care bundles rather than single prevention measures. A solid basis of knowledge seems necessary to facilitate implementation and maintain a high adherence level. Continuous educational efforts have a beneficial effect on attitude toward VAP. Intermittent subglottic secretions drainage, continuous lateral rotation therapy, and polyurethane cuffed endotracheal tubes decrease the risk of pneumonia. In an in-vitro setting, an endotracheal tube with a taper-shaped cuff appears to better prevent fluid leakage compared to cylindrical polyurethane or polyvinylchloride cuffed tubes. Cuff pressure control by means of an automatic device and multimodality chest physiotherapy need further investigation, as do some aspects of oral hygiene. Summary: New devices and strategies have been developed to prevent VAP. Some of these are promising but need further study. In addition, more attention is being given to factors that might facilitate the implementation process and the challenge of achieving high adherence rates

Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates

Results from the Clarify Study

Centro de Matemática e Aplicações, UID (MAT/00297/2020), Portuguese Foundation of Science and Technology. Publisher Copyright: © 2022 by the authors.Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.publishersversionpublishe

Proenkephalin A 119-159 (Penkid) Is an Early Biomarker of Septic Acute Kidney Injury: The Kidney in Sepsis and Septic Shock (Kid-SSS) Study

Introduction: Sepsis is the leading cause of acute kidney injury (AKI) in critically ill patients. The Kidney in Sepsis and Septic Shock (Kid-SSS) study evaluated the value of proenkephalin A 119-159 (penkid)—a sensitive biomarker of glomerular function, drawn within 24 hours upon intensive care unit (ICU) admission and analyzed using a chemiluminescence immunoassay—for kidney events in sepsis and septic shock. Methods: The Kid-SSS study was a substudy of Adrenomedullin and Outcome in Severe Sepsis and Septic Shock (AdrenOSS) (NCT02393781), a prospective, observational, multinational study including 583 patients admitted to the intensive care unit with sepsis or septic shock and a validation cohort of 525 patients from the French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) study. The primary endpoint was major adverse kidney events (MAKEs) at day 7, composite of death, renal replacement therapy, and persistent renal dysfunction. The secondary endpoints included AKI, transient AKI, worsening renal function (WRF), and 28-day mortality. Results: Median age was 66 years (interquartile range 55–75), and 28-day mortality was 22% (95% confidence interval [CI] 19%−25%). Of the patients, 293 (50.3%) were in shock upon ICU admission. Penkid was significantly elevated in patients with MAKEs, persistent AKI, and WRF (median = 65 [IQR = 45–106] vs. 179 [114–242]; 53 [39–70] vs. 133 [79–196] pmol/l; and 70 [47–121] vs. 174 [93–242] pmol/l, all P &lt; 0.0001), also after adjustment for confounding factors (adjusted odds ratio = 3.3 [95% CI = 1.8–6.0], 3.9 [95% CI = 2.1–7.2], and 3.4 [95% CI = 1.9–6.2], all P &lt; 0.0001). Penkid increase preceded elevation of serum creatinine with WRF and was low in renal recovery. Conclusion: Admission penkid concentration was associated with MAKEs, AKI, and WRF in a timely manner in septic patients