Construcción y validación de un modelo predictivo de hipotermia intraoperatoria

Rincón, David; Valero, Jordi Feo; Eslava-Schmalbach, Javier

doi:10.1016/s0034-9356(08)70610-8

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…Moreover, males have a higher risk of developing hypothermia than women due to their physical characteristics and rate of metabolic heat production 18 . Preoperative hypothermia is a predictor for a more severe decrease in body temperature intraoperatively 12,19 . If the temperature is unknown before surgery, active measures for reversing hypothermia could be delayed.…”

Section: Discussionmentioning

confidence: 99%

Prospective observational study of the effectiveness of prewarming on perioperative hypothermia in surgical patients submitted to spinal anesthesia

Becerra

Valencia

Ferrando

et al. 2019

Sci Rep

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Prewarming has been shown to prevent intraoperative inadvertent hypothermia. Nevertheless, data about optimal prewarming-time from published clinical trials report contradictory results. We conducted this pilot study to evaluate routine clinical practice regarding prewarming and its effect on the prevalence of perioperative hypothermia in patients undergoing transurethral resection (TUR) under spinal anesthesia. This was a prospective, observational, pilot study to examine clinical practice in a tertiary hospital regarding prewarming in 140 consecutive patients. When prewarming (pw) was performed, forced-air warming was provided in the pre-anesthesia room for 15 (pw15), 30 (pw30), or 45 (pw45) min. Tympanic temperature was recorded upon entering the pre-anesthesia room, at the time of initiating surgery, and every 15 min intra-operatively. We also recorded duration of the surgical procedure and length of stay in the Post-Anesthesia Care Unit (PACU). Pw15 was performed in 34 patients, pw30 in 29 patients, and pw45 in 21 patients. Fifty-six patients did not receive pw and 96% of them developed hypothermia at the end of the surgical procedure, compared to 73% of patients in pw15 (p = 0.002), 75% in pw30 (p = 0.006) and 90% in pw45 (p = 0.3). Length of stay in the PACU was markedly shorter in pw15 (131 ± 69 min) and pw30 (123 ± 60 min) than in the non-pw group (197 ± 105 min) (p = 0.015 and p = 0.011, respectively). This difference was not significant in pw45 (129 ± 56 min) compared to non-pw patients. In conclusion, prewarming for 15 or 30 min before TUR under spinal anesthesia prevents development of hypothermia at the end of the surgical procedure.

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Section: Discussionmentioning

confidence: 99%

Prospective observational study of the effectiveness of prewarming on perioperative hypothermia in surgical patients submitted to spinal anesthesia

Becerra

Valencia

Ferrando

et al. 2019

Sci Rep

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“…Half of the studies (n = 4) were conducted in Asian countries, 2,[19][20][21] and the rest originated from Europe (n = 3) or North America (n = 1). 13,[22][23][24] The oldest model was reported in 2002, 19 and the newest one in 2019. 24 The majority of the models (n = 6) were developed using data from prospective cohort studies, although retrospective data (n = 2) were also used.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…24 The majority of the models (n = 6) were developed using data from prospective cohort studies, although retrospective data (n = 2) were also used. 13,19 In addition, most studies were conducted with patients undergoing general anesthesia, 2,21,22 though surgeries with other types of anesthesia were also included, such as abdominal surgery, cesarean delivery, thoracic surgery, and open hysterectomy. 13,19,20,23,24 The number of predictors per study ranged from 3 to 8, and 18 different predictors were identified in the prediction models.…”

Section: Study Characteristicsmentioning

confidence: 99%

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Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review

Yan

Yao

Zhao

et al. 2021

Journal of PeriAnesthesia Nursing

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“…Several recent studies have researched in depth into these studies, taking advantage of behavioural registration of participants in sport programmes over periods longer than one year, using these variables to be able to create prediction models to be applied to other users (McDonald, Karg & Leckie 2014;Sperandei et al, 2016). For this type of analysis, different variants of logistic variants are used (León & Linares, 2014;Rincón, Valero & Eslava-Schmalbach, 2008). However, these studies are limited to creating the statistical models that estimate the dependent variable with the best fit, discussing the coefficients and weights of each of the factors that make up the regression.…”

Section: Introductionmentioning

confidence: 99%

Drop out prediction in sport centres. Definition of models and reproducibility (Predicción de bajas de clients en centros deportivos. Definición de modelos y reproducibilidad)

Clavel

García-Unanue

Iglesias-Soler

et al. 2019

Retos

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Abstract. Purpose: One of the main problems detected in the sport centre field is abandonment of physical activity by members. The aim of this estudy is to evaluate the reproducibility of predictive models of abandonment in sports centres, based on information collected through access control and the Customer Relationship Management system. Method: Monthly data corresponding to the actual behaviour of of two sport centres in Spain were recorded over the course of one year (12,545 members). Based on these variables, two predictive models were designed using the absence or permanence in the centre as an independent variable. Subsequently, the regression formula obtained was applied on all members of the same sports centres a year later (12,986 members), checking the effectiveness and reproducibility of the model. Results:. Logistic regression models based on customer historical behaviour were useful to predict abandonment. The effectiveness of the models was around 70% a year after. Conclusions: The segmentation of users based on the likelihood of drop out and the identification of behaviour variables that predict such drop out and the confirmation of the effectiveness of the predictive model, can help to optimize the design of loyalty strategies to prevent the abandonment in sport centres.Resumen. Objetivo: El abandono de la práctica deportiva por parte de clientes en centros deportivos es uno de los principals problemas en el sector. El objetivo de este estudio es evaluar la reproducibilidad de modelos predictivo de abandono en centros deportivos, basados en información recogida a través del control de accesos y el sistema Customer Relationship Management. Método: Se recopiló comportamientos mensuales de los clientes de dos centros deportivos en España durante un año (12,545 members). En base a dichas variables se diseñaron dos modelos predictivos utilizando la baja o permanencia en el centro como variable independiente. Posteriormente, se aplicó la formula de regression obtenida sobre todos los miembros de los mismos centros deportivos un año después (12,986 members), comprobando la efectividad y reproducibilidad del modelo. Resultados: Las estimaciones de regression logística fueron útiles para predecir el abandon en los centros deportivos. Además, reproduciendo el mismo modelos en una muestra diferente se consiguió aproximadamente un 70% de efectividad. Conclusiones: La segmentación de usuarios en base a la probabilidad de baja y la identificación de variables predictivas de dicha baja pueden optimizar el diseño de estrategias de prevención de abanadono en centros deportivos.

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Construcción y validación de un modelo predictivo de hipotermia intraoperatoria

Cited by 10 publications

References 7 publications

Prospective observational study of the effectiveness of prewarming on perioperative hypothermia in surgical patients submitted to spinal anesthesia

Prospective observational study of the effectiveness of prewarming on perioperative hypothermia in surgical patients submitted to spinal anesthesia

Risk Prediction Models for Inadvertent Intraoperative Hypothermia: A Systematic Review

Drop out prediction in sport centres. Definition of models and reproducibility (Predicción de bajas de clients en centros deportivos. Definición de modelos y reproducibilidad)

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