EDSON BOR-SENG SHU
Projetos de Pesquisa
Unidades Organizacionais
Instituto Central, Hospital das Clínicas, Faculdade de Medicina - Médico
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
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- Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population(2020) AMORIM, Robson Luis; OLIVEIRA, Louise Makarem; MALBOUISSON, Luis Marcelo; NAGUMO, Marcia Mitie; SIMOES, Marcela; MIRANDA, Leandro; BOR-SENG-SHU, Edson; BEER-FURLAN, Andre; ANDRADE, Almir Ferreira De; RUBIANO, Andres M.; TEIXEIRA, Manoel Jacobsen; KOLIAS, Angelos G.; PAIVA, Wellingson SilvaBackground: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in Sao Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members.
- Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability?(2020) ELTING, Jan Willem; SANDERS, Marit L.; PANERAI, Ronney B.; ARIES, Marcel; BOR-SENG-SHU, Edson; CAICEDO, Alexander; CHACON, Max; GOMMER, Erik D.; HUFFEL, Sabine Van; JARA, Jose L.; KOSTOGLOU, Kyriaki; MAHDI, Adam; MARMARELIS, Vasilis Z.; MITSIS, Georgios D.; MULLER, Martin; NIKOLIC, Dragana; NOGUEIRA, Ricardo C.; PAYNE, Stephen J.; PUPPO, Corina; SHIN, Dae C.; SIMPSON, David M.; TARUMI, Takashi; YELICICH, Bernardo; ZHANG, Rong; CLAASSEN, Jurgen A. H. R.We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC< 0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.
- Estimation of intracranial pressure by ultrasound of the optic nerve sheath in an animal model of intracranial hypertension(2021) JENG, Brasil Chian Ping; ANDRADE, Almir Ferreira de; BRASIL, Sergio; BOR-SENG-SHU, Edson; BELON, Alessandro Rodrigo; ROBERTIS, Maira; DE-LIMA-OLIVEIRA, Marcelo; RUBIANO, Andres Mariano; GODOY, Daniel Agustin; TEIXEIRA, Manoel Jacobsen; PAIVA, Wellingson SilvaBackground: Ultrasound of the optic nerve sheath diameter (ONSD) has been used as a non-invasive and cost-effective bedside alternative to invasive intracranial pressure (ICP) monitoring. However, ONSD time-lapse behavior in intracranial hypertension (ICH) and its relief by means of either saline infusion or surgery are still unknown. The objective of this study was to correlate intracranial pressure (ICP) and ultrasonography of the optic nerve sheath (ONS) in an experimental animal model of ICH and deter-mine the interval needed for ONSD to return to baseline levels. Methods: An experimental study was conducted on 30 pigs. ONSD was evaluated by ultrasound at differ-ent ICPs generated by intracranial balloon inflation, saline infusion, and balloon deflation, and measured using an intraventricular catheter. Results: All variables obtained by ONS ultrasonography such as left, right, and average ONSD (AON) were statistically significant to estimate the ICP value. ONSD changed immediately after balloon inflation and returned to baseline after an average delay of 30 min after balloon deflation (p = 0.016). No statistical sig-nificance was observed in the ICP and ONSD values with hypertonic saline infusion. In this swine model, ICP and ONSD showed linear correlation and ICP could be estimated using the formula:-80.5 + 238.2 x AON. Conclusion: In the present study, ultrasound to measure ONSD showed a linear correlation with ICP, although a short delay in returning to baseline levels was observed in the case of sudden ICH relief.
- Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study(2018) SANDERS, Marit L.; CLAASSEN, Jurgen A. H. R.; ARIES, Marcel; BOR-SENG-SHU, Edson; CAICEDO, Alexander; CHACON, Max; GOMMER, Erik D.; HUFFEL, Sabine Van; JARA, Jose L.; KOSTOGLOU, Kyriaki; MAHDI, Adam; MARMARELIS, Vasilis Z.; MITSIS, Georgios D.; MULLER, Martin; NIKOLIC, Dragan A.; NOGUEIRA, Ricardo C.; PAYNE, Stephen J.; PUPPO, Corina; SHIN, Dae C.; SIMPSON, David M.; TARUMI, Takashi; YELICICHS, Bernardo; ZHANG, Rong; PANERAI, Ronney B.; ELTING, Jan Willem J.Objective: Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. Approach: Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). Main results: For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). Significance: When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.
- Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability(2019) SANDERS, Marit L.; ELTING, Jan Willem J.; PANERAI, Ronney B.; ARIES, Marcel; BOR-SENG-SHU, Edson; CAICEDO, Alexander; CHACON, Max; GOMMERS, Erik D.; HUFFEL, Sabine Van; JARA, Jose L.; KOSTOGLOU, Kyriaki; MANDI, Adam; MARMARELIS, Vasilis Z.; MITSIS, Georgios D.; MULLER, Martin; NIKOLIC, Dragana; NOGUEIRA, Ricardo C.; PAYNE, Stephen J.; PUPPO, Corina; SHIN, Dae C.; SIMPSON, David M.; TARUMI, Takashi; YELICICH, Bernardo; ZHANGS, Rong; CLAASSEN, Jurgen A. H. R.Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 +/- 0.057, phase 0.17 +/- 0.13) than for LF band (gain 0.59 +/- 0.078, phase 0.39 +/- 0.11, p <= 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 +/- 0.12 and for the correlation methods 0.24 +/- 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.