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Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48 h estimation time and 8-16 h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0 L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.
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This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.
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The Mediterranean region is expected to be highly impacted by global warming, although the uncertainty of future scenarios, particularly about precipitation patterns remains quite large. To better predict shifts in its current climate system and to test models, more regional climate records are needed spanning longer than the instrumental period. Here we provide a high-resolution reconstruction of autumn precipitation for the Central Pyrenees since 1500 CE based on annual calcite sublayer widths from Montcortès Lake (Central southern Pyrenees) varved sediments. The 500-yr calcite data series was detrended and calibrated with instrumental climate records by applying correlations and cross-correlations to regional precipitation anomalies. Highest relationships were obtained between a composite calcite series and autumn precipitation anomalies for the complete calibration period (1900-2002) and for the two halves of the full period. Applied statistical tests were significant, evidencing that the climatic signal could be reconstructed. The reconstructed precipitation anomalies show interdecadal shifts, and rainfall decrease within the coldest period of the LIA and during the second half of the 20th century, probably associated to current Global Warming. Neither increasing nor decreasing linear trends or periods of extreme precipitation events were identified. Our results are coherent with other palaeohydrological reconstructions for northern Iberian Peninsula. Correlations between the predicted autumn precipitation and the main teleconnections -NAO, ENSO and WEMO- were weak, although a potential relationship with the Atlantic Multidecadal Oscillation (AMO) pattern is suggested. The obtained reconstruction provides the first estimations of regional autumn precipitation shifts in the Central Pyrenees and is one of the few reconstructions that cover annual-to-century scale climate variability of precipitation in the Mediterranean region from the end of the Litte Ice Age (LIA) to the current period of Global Warming.
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Carbonato de Calcio , Lagos , Calentamiento Global , Región Mediterránea , Estaciones del AñoRESUMEN
Previous studies suggest that there exists a lag relationship between daily milk yield and heat stress. The values of heat stress indicators (e.g., temperature-humidity index and ambient temperature) before test day have a simple correlation with daily milk yield on test day. However, the simple correlation might not be the best description because daily milk yield and heat stress indicators have a nature of time series in common, and their correlations are cross correlations that could be affected by autocorrelations. We hope to give a more reliable estimation on the lag relationship of daily milk yield via excluding autocorrelations with transfer function modeling. In this study, we found a lag relationship between daily milk yield and heat stress indicators based on transfer function modeling. Heat stress indicators included ambient temperature and temperature-humidity index. The daily milk yield data from 123 cows were obtained during a consecutive 63-d period (July 10-September 10, 2016). The mean daily milk yield (MY) and the maximum daily ambient temperature (TA_max) satisfied the stationary hypothesis, and the cross correlation between them was calculated. Before excluding autocorrelation, MY at 0 to 4 d after test day had significant cross correlations with TA_max on test day. After excluding the influence of autocorrelations, MY at 1 to 3 d after the test day had significant cross correlations with TA_max on test day. This result suggested that MY would respond to TA_max 1 d after the test day. In addition, the strength of cross correlations between MY and TA_max decreased from 1 to 3 d in sequence, implying a declining lag response of MY that would last for 3 d. The transfer function model for MY and TA_max is written as: MYt = 16.90 + 0.74MYt- 1 - 0.25TA_maxt- 1 + Nt, where Nt is white noise. This model can be used to track and predict the dynamic response of MY to TA_max.