Person:
Sánchez Luna, Manuel Ramón

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First Name
Manuel Ramón
Last Name
Sánchez Luna
URI
https://hdl.handle.net/20.500.14352/79227
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Medicina
Department
Salud Pública y Materno-Infantil
Area
Pediatría
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2018 - 20202
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    Spanish population‐study shows that healthy late preterm infants had worse outcomes one year after discharge than term‐born infants
    (Acta Paediatrica: Nurturing the Child, 2018) Sánchez Luna, Manuel Ramón; Fernández Pérez, Cristina; Bernal, José Luis; Elola Somoza, Francisco Javier
    Aim: This study assessed the risks associated with healthy late preterm infants and healthy term-born infants using national hospital discharge records. Method: We used the minimum basic data set of the Spanish hospital discharge records database for 2012-2013 to analyse the hospitalisation of newborn infants. The outcomes were in-hospital mortality and hospital re-admissions at 30 days and one year after their first discharge. Results: Of the 95 011 newborn infants who were discharged, 2940 were healthy late preterm infants, born at 34 + 0-36 + 6 weeks, and 18 197 were healthy term-born infants. The mean and standard deviation (SD) length of hospital stay were 6.0 (4.5) days in late preterm infants versus 2.8 (1.3) days in term-born infants (p < 0.001). Re-admissions were also higher in the late preterm group at 30 days (9.0% versus 4.4%) and one year (22.0% versus 12.4) (p < 0.001). The relative risk for death at one year was 4.9 in the late preterm group, when compared to the term-born infants (p = 0.026). Conclusion: The hospital discharge codes for otherwise healthy newborn preterm infants were associated with significantly worse 30-day and one-year outcomes when their re-admission and mortality rates were compared with healthy term-born newborn infants
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    Longitudinal Analysis of Continuous Pulse Oximetry as Prognostic Factor in Neonatal Respiratory Distress
    (American Journal of Perinatology, 2020) Solís García, Gonzalo; Maderuelo Rodríguez, Elena; Pérez Pérez, Teresa; Torres Soblechero, Laura; Gutiérrez Vélez, Ana; Ramos Navarro, Cristina; López Martínez, Raúl; Sánchez Luna, Manuel Ramón
    Objective: Analysis of longitudinal data can provide neonatologists with tools that can help predict clinical deterioration and improve outcomes. The aim of this study is to analyze continuous monitoring data in newborns, using vital signs to develop predictive models for intensive care admission and time to discharge. Study design: We conducted a retrospective cohort study, including term and preterm newborns with respiratory distress patients admitted to the neonatal ward. Clinical and epidemiological data, as well as mean heart rate and saturation, at every minute for the first 12 hours of admission were collected. Multivariate mixed, survival and joint models were developed. Results: A total of 56,377 heart rate and 56,412 oxygen saturation data were analyzed from 80 admitted patients. Of them, 73 were discharged home and 7 required transfer to the intensive care unit (ICU). Longitudinal evolution of heart rate (p < 0.01) and oxygen saturation (p = 0.01) were associated with time to discharge, as well as birth weight (p < 0.01) and type of delivery (p < 0.01). Longitudinal heart rate evolution (p < 0.01) and fraction of inspired oxygen at admission at the ward (p < 0.01) predicted neonatal ICU (NICU) admission. Conclusion: Longitudinal evolution of heart rate can help predict time to transfer to intensive care, and both heart rate and oxygen saturation can help predict time to discharge. Analysis of continuous monitoring data in patients admitted to neonatal wards provides useful tools to stratify risks and helps in taking medical decisions. Key points: · Continuous monitoring of vital signs can help predict and prevent clinical deterioration in neonatal patients.. · In our study, longitudinal analysis of heart rate and oxygen saturation predicted time to discharge and intensive care admission.. · More studies are needed to prospectively prove that these models can helpmake clinical decisions and stratify patients' risks..