Equity matrix for kidney transplant allocation.

There is a general agreement that the distribution of kidneys for transplantation should balance utility criteria with justice. Moreover, a kidney allocation system must be based on transparent policies and seen as an ongoing process. This study aims to present an allocation system grounded on an equity matrix that balances the criteria of utility and justice. Synthetic data for a waiting list with 2000 transplant candidates and a pool of 280 donors were generated. A color priority system, the Eurotransplant (ET) kidney allocation system, and the proposed Equity Matrix (EQM) allocation system were compared after 1000 iterations of kidney allocations. Distributions of variables like the age difference, Human Leukocyte Antigens (HLA) mismatches (mmHLA), recipients' time on dialysis, cPRA, and a transplant score obtained by different allocation models were compared graphically and with Cohen's d effect size. For the analyzed variables, when we compare only the selected recipients from ET with the selected recipients from the EQM neutral model, we can conclude that the former model selects more hypersensitized recipients, a higher number of 65+ years' old recipients with 65+ years' old donors and higher number of recipients with 0 mmHLA. While recipients from EQM neutral are slightly older, have a lower age difference with their donors, have a lower number of mmHLA, are less likely to have 6 mmHLA with their donors, and have more time on dialysis. The proposed EQM model attempts to provide a simple, transparent, and equitable response to a complex question with results that outperform established practices.

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses-Porto retrospective intrapartum study.

Introduction: Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model. Objectives: This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices. Methods: Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models. Results: The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%]. Conclusion: Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Characterization of an obese population: a retrospective longitudinal study from real-world data in northern Portugal.

BACKGROUND: Obesity is a serious and largely preventable global health problem. Obesity-related electronic health records can be a useful resource to identify and address obesity. The analysis of real-world data from T82-coded (International Classification of Primary Care coding, for obesity) primary care individuals can be an excellent national source of data on obesity's prevalence, characteristics, and impact on the National Health Service. METHODS: Retrospective longitudinal study, based on a database of electronic medical records, from the Regional Health Administration of northern Portugal. The study objectives were to determine the prevalence of obesity and to characterize an adult obese population in northern Portugal from a bio-demographic point of view along with profiles of comorbidities and the use of health resources. This study used a database of 266,872 patients in December 2019 and screened for diagnostic code T82 from the International Classification of Primary Care. RESULTS: The prevalence of obesity was 10.2% and the highest prevalence of obesity was in the 65-74 age group (16.1%). The most prevalent morbidities in patients with obesity as coded through ICPC-2 were K86 (uncomplicated hypertension), T90 (non-insulin-dependent diabetes), and K87 (complicated hypertension). Descriptive information showed that T82 subjects used more consultations, medications, and diagnostic tests than non-T82 subjects. CONCLUSIONS: Routine recording of weight and height deserves special attention to allow obesity recognition at an early stage and move on to the appropriate intervention. Future work is necessary to automate the codification of obesity for subjects under 18 years of age, to raise awareness and anticipate the prevention of problems associated with obesity. Practical strategies need to be implemented, such as the creation of a specific program consultation with truly targeted approaches to obesity.

Dysfunction of norepinephrine and its metabolites in Alzheimer's dementia - A review with meta-analysis.

Some studies point locus coeruleus cell loss, the central nervous system main source of norepinephrine, to be one of the earliest neuropathological events of Alzheimer's disease (AD). However, there are conflicting reports regarding the level of norepinephrine and its metabolites (3-Methoxy-4-hydroxyphenylglycol (MHPG), 3,5-dihydroxyphenylglycine (DHPG) and 3,4 -dihydroxyphenylglycolaldehyde (DOPEGAL)) in AD patients. Uncover these alterations may be a key factor for understanding cognitive deficits and AD pathology. We review the literature that compare norepinephrine and its metabolites between AD patients and non-demented controls. A meta-analysis did not reveal significant statistical differences, but there was a trend towards a lower level of norepinephrine of AD, with almost no difference in MHPG in the cerebrospinal fluid. Regarding MHPG in plasma, DHPG and DOPEGAL we only performed a qualitative analyse due to the small or absent number of studies. These findings point to a decrease in norepinephrine, what is in line with locus coeluleus cell loss in AD. The absence of statistical difference and an equal level of MHGP could indicate a compensatory mechanism.

Evolution of Heart Rate Complexity Indices in the Early Detection of Neonatal Sepsis.

Despite advances in prenatal health care, neonatal sepsis remains a major cause of neonatal mortality. Early diagnosis and adequate treatment are essential to reduce morbidity and mortality related to this disease. In this paper, we propose a new method to detect neonatal sepsis based on heart rate (HR) complexity measures (entropy and compression indices) that takes into consideration neonatal gestational age. First, the percentile curves were computed for all the complexity indices using data from 118 control neonates. Eight indices were computed: the sample entropy (SampEn) and three indices to quantify the multiscale entropy (MSE) curve - the sum, the slope, and the product of the previous two - and the compression ratio (CR), using the bzip2 compressor, as well as the same three indices but related to the multiscale compression (MSC) curve. Then, the corresponding percentile was estimated for 23 sepsis neonates. Results show a significant decrease in the entropy indices SampEn and MSEsum and in the MSCslope a day before the detection of sepsis by the clinicians. The indices CR and MSCsum increased before the antibiotic take. These results imply that sepsis causes a random, uncorrelated pattern on the HR signal. Future studies should include a bigger data set to calculate a compound index comprising information of other physiological signals. Clinical Relevance - Prompt and accurate diagnosis of neona-tal sepsis is essential for the successful clinical management of neonates and significantly reduce morbidity and mortality. Complexity measures applied to the HR time series appear to detect sepsis in neonates starting one day before the clinical detection.

Kidney allocation rules simulator.

The greatest challenge of any kidney transplant program lies in finding enough organ donors (in number and quality) for all waitlisted transplant candidates. Unfortunately, we must resign ourselves to a manifestly insufficient supply of organs for the current demand. Furthermore we must be able to predict kidney transplant success at organ allocation if we want to minimize the number of patients who return to an already overcrowded waiting list for transplantation. Therefore, the definition of deceased donors' kidney allocation rules on transplantation must be supported by simulations that allow foreseeing, as much as possible, the consequences of these rules. Here we present the Kidney Allocation Rules Simulator (KARS) application that enables testing different kidney transplant allocation' systems with different donors and transplant candidates' datasets. In this application, it is possible to simulate allocation rules implemented in Portugal, in the United Kingdom, in countries within Eurtotransplant, and a previously suggested color priority system. As inputs, this application needs three data files: a file with transplant candidates' information, a file with candidates' anti-HLA antibodies, and a file with donors' information. As output, we will have a file with donor-recipient pairs selected according to the kidney allocation system simulated. When seeking waste reduction while ensuring a fair distribution of organs from deceased donors, the definition of rules selecting donor-recipient pairs in renal transplantation must be based on evidence supported by data. On the continuously changing process for a better distribution of an increasingly scarce resource must, we must be able to predict transplant outcomes when defining the best allocation rules.

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review.

The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

COVID-19 surveillance data quality issues: a national consecutive case series.

OBJECTIVES: High-quality data are crucial for guiding decision-making and practising evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese epidemiological surveillance dataset, our study aims to assess COVID-19 data quality issues and suggest possible solutions. SETTINGS: On 27 April 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On 4 August, an updated dataset (DGSAugust) was also obtained. PARTICIPANTS: All COVID-19-confirmed cases notified through the medical component of National System for Epidemiological Surveillance until end of June. PRIMARY AND SECONDARY OUTCOME MEASURES: Data completeness and consistency. RESULTS: DGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (eg, 4075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (eg, the variable 'underlying conditions' had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily. CONCLUSIONS: Important quality issues of the Portuguese COVID-19 surveillance datasets were described. These issues can limit surveillance data usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed-for example, simplification of data entry processes, constant monitoring of data, and increased training and awareness of healthcare providers-as low data quality may lead to a deficient pandemic control.

What role do patients prefer in medical decision-making?: a population-based nationwide cross-sectional study.

OBJECTIVE: To assess patients' preferred roles in healthcare-related decision-making in a representative sample of the Portuguese population. DESIGN: Population-based nationwide cross-sectional study. SETTING AND PARTICIPANTS: A sample of Portuguese people 20 years or older were interviewed face-to-face using a questionnaire with the Problem-Solving Decision-Making scale. OUTCOMES: The primary outcome was patients' preferred role for each vignette of the problem-solving decision-making scale. Sociodemographic factors associated with the preferred roles were the secondary outcomes. RESULTS: 599 participants (20-99 years, 53.8% women) were interviewed. Three vignettes of the Problem-Solving Decision-Making scale were compared: morbidity, mortality and quality of life. Most patients preferred a passive role for both the problem-solving and decision-making components of the scale, particularly for the mortality vignette (66.1% in the analysis of the three vignettes), although comparatively more opted to share decision in the decision-making component. For the quality of life vignette, a higher percentage of patients wanted a shared role (44.3%) than with the other two vignettes. In the problem-solving component, preferences were significantly associated with area of residence (p<0.001) and educational level (p=0.013), while in the decision-making, component preferences were associated with age (p=0.020), educational level (p=0.015) and profession (p<0.001). CONCLUSIONS: In this representative sample of the Portuguese mainland population, most patients preferred a practitioner-controlling role for both the problem-solving and decision-making components. In a life-threatening situation, patients were more willing to let the doctor decide. In contrast, in a less serious situation, there is a greater willingness to participate in decision-making. We have found that shared decision-making is more acceptable to better-educated patients in the problem-solving component and to people who are younger, higher educated and employed, in the decision-making component.

Fetal Heart Rate Fragmentation.

Intrapartum fetal monitoring's primary goal is to avoid adverse perinatal outcomes related to hypoxia/acidosis without increasing unnecessary interventions. Recently, a set of indices were proposed as new biomarkers to analyze heart rate (HR), termed HR fragmentation (HRF). In this work, the HRF indices were applied to intrapartum fetal heart rate (FHR) traces to evaluate fetal acidemia. The fragmentation method produces four indices: PIP-Percentage of inflection points; IALS-Inverse of the average length of acceleration/deceleration segments; PSS-Percentage of short segments; PAS-Percentage of alternating segments. On the other hand, the symbolic approach studied the existence of different patterns of length four. We applied the measures to 246 selected FHR recordings sampled at 4 and 2 Hz, where 39 presented umbilical artery's pH ≤ 7.15. When applied to the 4 Hz FHR, the PIP, IASL, and PSS showed significantly higher values in the traces from acidemic fetuses. In comparison, the percentage of "words" W 1 h and W 2 s showed lower values for those traces. Furthermore, when using the 2 Hz, only IASL, W 0, and W 2 m achieved significant differences between traces from both acidemic and normal fetuses. Notwithstanding, the ideal sampling frequency is yet to be established. The fragmentation indices correlated with Sisporto variability measures, especially short-term variability. Accordingly, the fragmentation indices seem to be able to detect pathological patterns in FHR tracings. These indices have the advantage of being suitable and straightforward to apply in real-time analysis. Future studies should combine these indexes with others used successfully to detect fetal hypoxia, improving the power of discrimination in a larger dataset.

When Problems Only Get Bigger: The Impact of Adverse Childhood Experience on Adult Health.

Introduction: Previous studies have shown that adverse childhood experiences negatively impact child development, with consequences throughout the lifespan. Some of these consequences include the exacerbation or onset of several pathologies and risk behaviors. Materials and Methods: A convenience sample of 398 individuals aged 20 years or older from the Porto metropolitan area, with quotas, was collected. The evaluation was conducted using an anonymous questionnaire that included sociodemographic questions about exposure to adverse childhood experiences, a list of current health conditions, questions about risk behaviors, the AUDIT-C test, the Fagerström test and the Childhood Trauma Questionnaire-brief form. Variables were quantified to measure adverse childhood experiences, pathologies, and risk behaviors in adult individuals for comparison purposes. Results: Individuals with different forms of adverse childhood experiences present higher rates of smoking dependence, self-harm behaviors, victimization of/aggression toward intimate partners, early onset of sexual life, sexually transmitted infections, multiple sexual partners, abortions, anxiety, depression, diabetes, arthritis, high cholesterol, hypertension, and stroke. Different associations are analyzed and presented. Discussion and Conclusions: The results show that individuals with adverse childhood experiences have higher total scores for more risk behaviors and health conditions than individuals without traumatic backgrounds. These results are relevant for health purposes and indicate the need for further research to promote preventive and protective measures.

Systematic Review of Intrapartum Fetal Heart Rate Spectral Analysis and an Application in the Detection of Fetal Acidemia.

It is fundamental to diagnose fetal acidemia as early as possible, allowing adequate obstetrical interventions to prevent brain damage or perinatal death. The visual analysis of cardiotocography traces has been complemented by computerized methods in order to overcome some of its limitations in the screening of fetal hypoxia/acidemia. Spectral analysis has been proposed by several studies exploring fetal heart rate recordings while referring to a great variety of frequency bands for integrating the power spectrum. In this paper, the main goal was to systematically review the spectral bands reported in intrapartum fetal heart rate studies and to evaluate their performance in detecting fetal acidemia/hypoxia. A total of 176 articles were reviewed, from MEDLINE, and 26 were included for the extraction of frequency bands and other relevant methodological information. An open-access fetal heart rate database was used, with recordings of the last half an hour of labor of 246 fetuses. Four different umbilical artery pH cutoffs were considered for fetuses' classification into acidemic or non-acidemic: 7.05, 7.10, 7.15, and 7.20. The area under the receiver operating characteristic curve (AUROC) was used to quantify the frequency bands' ability to distinguish acidemic fetuses. Bands referring to low frequencies, mainly associated with neural sympathetic activity, were the best at detecting acidemic fetuses, with the more severe definition (pH ≤ 7.05) attaining the highest values for the AUROC. This study shows that the power spectrum analysis of the fetal heart rate is a simple and powerful tool that may become an adjunctive method to CTG, helping healthcare professionals to accurately identify fetuses at risk of intrapartum hypoxia and to implement timely obstetrical interventions to reduce the incidence of related adverse perinatal outcomes.

The Entropy Universe.

About 160 years ago, the concept of entropy was introduced in thermodynamics by Rudolf Clausius. Since then, it has been continually extended, interpreted, and applied by researchers in many scientific fields, such as general physics, information theory, chaos theory, data mining, and mathematical linguistics. This paper presents The Entropy Universe, which aims to review the many variants of entropies applied to time-series. The purpose is to answer research questions such as: How did each entropy emerge? What is the mathematical definition of each variant of entropy? How are entropies related to each other? What are the most applied scientific fields for each entropy? We describe in-depth the relationship between the most applied entropies in time-series for different scientific fields, establishing bases for researchers to properly choose the variant of entropy most suitable for their data. The number of citations over the past sixteen years of each paper proposing a new entropy was also accessed. The Shannon/differential, the Tsallis, the sample, the permutation, and the approximate entropies were the most cited ones. Based on the ten research areas with the most significant number of records obtained in the Web of Science and Scopus, the areas in which the entropies are more applied are computer science, physics, mathematics, and engineering. The universe of entropies is growing each day, either due to the introducing new variants either due to novel applications. Knowing each entropy's strengths and of limitations is essential to ensure the proper improvement of this research field.

AF-React study: atrial fibrillation management strategies in clinical practice-retrospective longitudinal study from real-world data in Northern Portugal.

OBJECTIVES: To determine the prevalence of atrial fibrillation (AF) and to assess how these patients are being cared for: what anticoagulants are being prescribed and are they being prescribed as recommended? DESIGN: Retrospective longitudinal study. SETTING: This study was conducted in the Regional Health Administration of Northern Portugal. PARTICIPANTS: This study used a database that included 63526 patients with code K78 of the International Classification of Primary Care between January 2016 and December 2018. RESULTS: The prevalence of AF among adults over 40 years in the northern region of Portugal was 2.3% in 2016, 2.8% in 2017 and 3% in 2018. From a total of 63 526 patients, 95.8% had an indication to receive anticoagulation therapy. Of these, 44 326 (72.9%) are being treated with anticoagulants: 17 936 (40.5%) were prescribed vitamin K antagonists (VKAs) and 26 390 (59.5%) were prescribed non-VKA anticoagulants. On the other hand, 2688 patients of the total (4.2%) had no indication to receive anticoagulation therapy. Of these 2688 patients, 1100 (40.9%) were receiving anticoagulants. CONCLUSIONS: The prevalence of AF is 3%. Here, we report evidence of both undertreatment and overtreatment. Although having an indication, a considerable proportion of patients (27.1%) are not anticoagulated, and among patients with AF without an indication to receive anticoagulation therapy, a considerable proportion (40.9%) are receiving anticoagulants. The AF-React study brings extremely relevant conclusions to Portugal and follows real-world studies in patients with AF in Europe, presenting some data not yet studied.

Complexity of Cardiotocographic Signals as A Predictor of Labor.

Prediction of labor is of extreme importance in obstetric care to allow for preventive measures, assuring that both baby and mother have the best possible care. In this work, the authors studied how important nonlinear parameters (entropy and compression) can be as labor predictors. Linear features retrieved from the SisPorto system for cardiotocogram analysis and nonlinear measures were used to predict labor in a dataset of 1072 antepartum tracings, at between 30 and 35 weeks of gestation. Two groups were defined: Group A-fetuses whose traces date was less than one or two weeks before labor, and Group B-fetuses whose traces date was at least one or two weeks before labor. Results suggest that, compared with linear features such as decelerations and variability indices, compression improves labor prediction both within one (C-Statistics of 0.728) and two weeks (C-Statistics of 0.704). Moreover, the correlation between compression and long-term variability was significantly different in groups A and B, denoting that compression and heart rate variability look at different information associated with whether the fetus is closer to or further from labor onset. Nonlinear measures, compression in particular, may be useful in improving labor prediction as a complement to other fetal heart rate features.

Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review.

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.

Changes in nonlinear dynamic complexity measures of blood pressure during anesthesia for cardiac surgeries using cardio pulmonary bypass.

Nonlinear complexity measures computed from beat-to-beat arterial BP dynamics have shown associations with standard cardiac surgical risk indices. They reflect the physiological adaptability of a system and has been proposed as dynamical biomarkers of overall health status. We sought to determine the impact of anesthetic induction and cardiopulmonary bypass (CPB) upon the complexity measures computed from perioperative BP time series. In this prospective, observational study, 300 adult patients undergoing cardiac surgery were included. Perioperative period was divided as: (1) Preoperative (PreOp); (2) ORIS-induction to sternotomy; (3) ORSB- sternotomy to CPB; (4) ORposB-post CPB and within 30 min before leaving OR and (5) postoperative phase (PostOp)-initial 30 min in the cardiac surgical intensive care unit. BP waveforms for systolic (SAP), diastolic (DAP), mean arterial pressure (MAP) and pulse pressure (PP) were recorded, and their corresponding complexity index (MSE∑) was calculated. Significant decrease in MSE∑ from Preop to PostOp phases was observed for all BP time series. Maximum fall was seen during post anesthetic induction (ORIS) phase. Mild recovery during the subsequent phases was observed but they never reached the baseline values. In an exploratory analysis, preoperative MSE∑ showed a significant correlation with postoperative length of ICU stay. Blood pressure complexity varies at different time points and is not fixed for a given individual. Preoperative BP Complexity decreased significantly following anesthetic induction and did not recover to baseline until 30 min after surgery. Prevention of this significant fall may offer restoration of MSE∑ throughout surgery. Furthermore, preoperative BP complexity needs to be explored as a predictor of major postoperative adverse events by itself or in addition with the current risk indices.

Preoperative Blood Pressure Complexity Indices as a Marker for Frailty in Patients Undergoing Cardiac Surgery.

OBJECTIVE: Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients. DESIGN: Prospective, observational study. SETTING: Single-center teaching hospital. PARTICIPANTS: Three hundred and sixty-four adult patients undergoing cardiac surgery. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSEΣ) median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE Σ demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64). CONCLUSION: Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

Comparison of Invasive and Noninvasive Blood Pressure Measurements for Assessing Signal Complexity and Surgical Risk in Cardiac Surgical Patients.

BACKGROUND: Continuous arterial blood pressure (ABP) is typically recorded by placement of an intraarterial catheter. Recently, noninvasive ABP monitors have been shown to be comparable in accuracy to invasive measurements. In a previous study, we showed that the fluctuations in beat-to-beat ABP measurements were not random variations but had a complex dynamical structure, and that ABP dynamical complexity was inversely associated with surgical risk estimated using the Society of Thoracic Surgeons (STS) index. Dynamical complexity is a mathematical construct that reflects the capacity of a physiological system to adapt to stimuli. The objectives of present study were to: (1) determine whether noninvasive beat-to-beat ABP measurements also exhibit a complex temporal structure; (2) compare the complexity of noninvasive versus invasive ABP time series; and (3) quantify the relationship between the complexity of noninvasive ABP time series and the STS risk scores. METHODS: Fifteen adult patients undergoing coronary artery bypass graft, valve, or combined coronary artery bypass graft/valve surgery were enrolled in this observational study. Preoperative ABP waveforms were simultaneously recorded for ≥15 minutes using a radial artery catheter (invasive) and a continuous noninvasive arterial pressure monitor. Beat-to-beat systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), and mean arterial pressure (MAP) time series were extracted from the continuous waveforms. Complexity was assessed using the multiscale entropy method. The Wilcoxon signed-rank test was used to compare the mean ranks of indices derived from invasive versus noninvasive ABP time series. Spearman correlation coefficients were used to quantify the relationship between invasive and noninvasive indices. Linear regression analysis was used to quantify the association between each of the complexity indices and the STS risk scores. RESULTS: Beat-to-beat fluctuations in noninvasive ABP measurements were not random but complex; however, their degree of complexity was lower than that of fluctuations in invasively obtained ABP signals (SBP: 7.05 vs 8.66, P < .001; DBP: 7.40 vs 8.41, P < .001; PP: 6.83 vs 8.82, P < .001; and MAP: 7.17 vs 8.68, P < .005). Invasive and noninvasive indices for MSEΣ·slope showed good correlation (rs) (0.53 for SBP, 0.79 for DBP, 0.42 for PP, 0.60 for MAP). The complexity of noninvasive ABP time series (-0.70 [-1.28 to -0.11]; P = .023 for DBP), like that of invasive time series (-0.94 [-1.52 to -0.35]; P = .004 for DBP), was inversely associated with estimated surgical risk in patients undergoing cardiovascular operations. CONCLUSIONS: Our results support the use of noninvasive ABP monitoring in computations of complexity-based indices that correlate with estimated surgical risk.

Complexity of preoperative blood pressure dynamics: possible utility in cardiac surgical risk assessment.

Complexity measures are intended to assess the cardiovascular system's capacity to respond to stressors. We sought to determine if decreased BP complexity is associated with increased estimated risk as obtained from two standard instruments: the Society of Thoracic Surgeons' (STS) Risk of Mortality and Morbidity Index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II). In this observational cohort study, preoperative systolic, diastolic, mean (MAP) and pulse pressure (PP) time series were derived in 147 patients undergoing cardiac surgery. The complexity of the fluctuations of these four variables was quantified using multiscale entropy (MSE) analysis. In addition, the traditional time series measures, mean and standard deviation (SD) were also computed. The relationships between time series measures and the risk indices (after logarithmic transformation) were then assessed using nonparametric (Spearman correlation, rs) and linear regression methods. A one standard deviation change in the complexity of systolic, diastolic and MAP time series was negatively associated (p < 0.05) with the STS and EuroSCORE indices in both unadjusted (21-34%) and models adjusted for age, gender and SD of the BP time series (15-31%). The mean and SD of BP time series were not significantly associated with the risk index except for a positive association with the SD of the diastolic BP. Lower preoperative BP complexity was associated with a higher estimated risk of adverse cardiovascular outcomes and may provide a novel approach to assessing cardiovascular risk. Future studies are needed to determine whether dynamical risk indices can improve current risk prediction tools.