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1.
Revista de la Academia Colombiana de Ciencias Exactas, Fisicas y Naturales ; 45(177):971-979, 2021.
Article in Spanish | Scopus | ID: covidwho-2111236

ABSTRACT

This work shows a comparison between the probability distributions for the cases confirmed by Covid-19 in the departments of Colombia for the three time intervals where there is a greater number of infections (peaks). This was done from the statistical analysis of the databases reported by the National Institute of Health in Colombia. It is found that the probability of dying has increased by more than 8 % in the last peak for Colombians between 20 and 69 years of age, being the departments with the highest increase in the percentage of these deaths: Amazonas, Antioquia, Caquetá, Cauca, Córdoba and Putumayo. © 2021 Colombian Academy of Exact, Physical and Natural Sciences. All rights reserved.

2.
Lancet Digital Health ; 4(8):E573-E583, 2022.
Article in English | Web of Science | ID: covidwho-2092794

ABSTRACT

Background Real-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level. Methods We report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020;40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021;43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk. Findings The framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0.89 [95% CI 0.88-0.90]) and similarly predictive using only contact-network variables (0.88 [0.86-0.90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0.82 [95% CI 0.80-0.84]) or patient clinical (0.64 [0.62-0.66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0.85 (95% CI 0.82-0.88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0.84 [95% CI 0.82-0.86] to 0.88 [0.86-0.90];AUC-ROC in the UK post-surge dataset increased from 0.49 [0.46-0.52] to 0.68 [0.64-0.70]). Interpretation Dynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

4.
International Journal of Infectious Diseases ; 116:S20-S20, 2022.
Article in English | PMC | ID: covidwho-1717728
5.
Revista Chilena de Anestesia ; 49(5):742-746, 2020.
Article in Spanish | Scopus | ID: covidwho-903276

ABSTRACT

Since the start of the COviD-19 pandemic, several anesthetic societies have generated clinical recommendations for the perioperative management of these patients, including the Chilean Society of Anesthesiology. Among these recommendations, the advantages of regional anesthesia have been highlighted. in this article, we report and discuss the case of a 59-year-old patient with diabetes mellitus ii, Chronic Arterial Hypertension, Gout, and Stage iv Chronic Renal Failure admitted with a multifocal septic condition characterized by suppurative collections including a large subcutaneous lumbar abscess recently drained. The patient evolved with left knee septic arthritis and was scheduled for arthroscopic irrigation and debridement. As per protocol a SARS-COv2 PCR was tested and resulted positive. it was decided to proceed to surgery under anesthetic ultrasound-guided femoral and sciatic nerve blocks using an adrenalized (2.5 ug/mL) solution of 0.33% Levobupivacaine- 0.66% Lidocaine (15 mL each). Fifteen minutes later, the knee was mobilized passively without pain. Surgery started after 30 minutes. The surgical and anesthetic conditions were described as adequate by the surgeon and the patient, respectively. The postoperative evolution was satisfactory without presenting respiratory symptoms and the patient was discharged 17 days after under oral antibiotic treatment. © 2020 Sociedad de Anestesiologia de Chile. All rights reserved.

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