Integrating electronic health data records to develop and validate a predictive model of hospital-acquired acute kidney injury in non-critically ill patients.

BACKGROUND: Models developed to predict hospital-acquired acute kidney injury (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow the establishment of a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI. METHODS: The study set was 36 852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21 545 non-critical patients admitted to the validation centre in the period from June 2017 to December 2018. RESULTS: The incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios (ORs) were conferred by chronic kidney disease, urologic disease and liver disease. Among acute complications, the highest ORs were associated with acute respiratory failure, anaemia, systemic inflammatory response syndrome, circulatory shock and major surgery. The model showed an area under the curve (AUC) of 0.907 [95% confidence interval (CI) 0.902-0.908), a sensitivity of 82.7 (95% CI 80.7-84.6) and a specificity of 84.2 (95% CI 83.9-84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (χ2 = 6.02, P = 0.64). In the validation set, the prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904-0.910), a sensitivity of 81.2 (95% CI 79.2-83.1) and a specificity of 82.5 (95% CI 82.2-83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (χ2 = 4.2, P = 0.83). An online tool (predaki.amalfianalytics.com) is available to calculate the risk of AKI in other hospital environments. CONCLUSIONS: By using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI during the hospital admission period in non-critically ill patients.

External validation of the Madrid Acute Kidney Injury Prediction Score.

BACKGROUND: The Madrid Acute Kidney Injury Prediction Score (MAKIPS) is a recently described tool capable of performing automatic calculations of the risk of hospital-acquired acute kidney injury (HA-AKI) using data from from electronic clinical records that could be easily implemented in clinical practice. However, to date, it has not been externally validated. The aim of our study was to perform an external validation of the MAKIPS in a hospital with different characteristics and variable case mix. METHODS: This external validation cohort study of the MAKIPS was conducted in patients admitted to a single tertiary hospital between April 2018 and September 2019. Performance was assessed by discrimination using the area under the receiver operating characteristics curve and calibration plots. RESULTS: A total of 5.3% of the external validation cohort had HA-AKI. When compared with the MAKIPS cohort, the validation cohort showed a higher percentage of men as well as a higher prevalence of diabetes, hypertension, cardiovascular disease, cerebrovascular disease, anaemia, congestive heart failure, chronic pulmonary disease, connective tissue diseases and renal disease, whereas the prevalence of peptic ulcer disease, liver disease, malignancy, metastatic solid tumours and acquired immune deficiency syndrome was significantly lower. In the validation cohort, the MAKIPS showed an area under the curve of 0.798 (95% confidence interval 0.788-0.809). Calibration plots showed that there was a tendency for the MAKIPS to overestimate the risk of HA-AKI at probability rates ˂0.19 and to underestimate at probability rates between 0.22 and 0.67. CONCLUSIONS: The MAKIPS can be a useful tool, using data that are easily obtainable from electronic records, to predict the risk of HA-AKI in hospitals with different case mix characteristics.

Development and Validation of a Model to Predict Severe Hospital-Acquired Acute Kidney Injury in Non-Critically Ill Patients.

BACKGROUND: The current models developed to predict hospital-acquired AKI (HA-AKI) in non-critically ill fail to identify the patients at risk of severe HA-AKI stage 3. OBJECTIVE: To develop and externally validate a model to predict the individual probability of developing HA-AKI stage 3 through the integration of electronic health databases. METHODS: Study set: 165,893 non-critically ill hospitalized patients. Using stepwise logistic regression analyses, including demography, chronic comorbidities, and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI stage 3. This model was then externally validated in 43,569 non-critical patients admitted to the validation center. RESULTS: The incidence of HA-AKI stage 3 in the study set was 0.6%. Among chronic comorbidities, the highest odds ratios were conferred by ischemic heart disease, ischemic cerebrovascular disease, chronic congestive heart failure, chronic obstructive pulmonary disease, chronic kidney disease and liver disease. Among acute complications, the highest odd ratios were associated with acute respiratory failure, major surgery and exposure to nephrotoxic drugs. The model showed an AUC of 0.906 (95% CI 0.904 to 0.908), a sensitivity of 89.1 (95% CI 87.0-91.0) and a specificity of 80.5 (95% CI 80.2-80.7) to predict HA-AKI stage 3, but tended to overestimate the risk at low-risk categories with an adequate goodness-of-fit for all risk categories (Chi2: 16.4, p: 0.034). In the validation set, incidence of HA-AKI stage 3 was 0.62%. The model showed an AUC of 0.861 (95% CI 0.859-0.863), a sensitivity of 83.0 (95% CI 80.5-85.3) and a specificity of 76.5 (95% CI 76.2-76.8) to predict HA-AKI stage 3 with an adequate goodness of fit for all risk categories (Chi2: 15.42, p: 0.052). CONCLUSIONS: Our study provides a model that can be used in clinical practice to obtain an accurate dynamic assessment of the individual risk of HA-AKI stage 3 along the hospital stay period in non-critically ill patients.

Novel Measure of Local Impedance Predicts Catheter-Tissue Contact and Lesion Formation.

BACKGROUND: Coupling between the ablation catheter and myocardium is critical to resistively heat tissue with radiofrequency ablation. The objective of this study was to evaluate whether a novel local impedance (LI) measurement on an ablation catheter identifies catheter-tissue coupling and is predictive of lesion formation. METHODS AND RESULTS: LI was studied in explanted hearts (n=10 swine) and in vivo (n=10; 50-70 kg swine) using an investigational electroanatomic mapping system that measures impedance from an ablation catheter with mini-electrodes incorporated in the distal electrode (Rhythmia and IntellaNav MiFi OI, Boston Scientific). Explanted tissue was placed in a warmed (37 °C) saline bath mounted on a scale, and LI was measured 15 mm away from tissue to 5 mm of catheter-tissue compression at multiple catheter angles. Lesions were created with 31 and 50 W for 5 to 45 seconds (n=90). During in vivo evaluation of LI, measurements of myocardium (n=90) and blood pool (n=30) were guided by intracardiac ultrasound while operators were blinded to LI data. Lesions were created with 31 and 50 W for 45 seconds in the ventricles (n=72). LI of myocardium (119.7 Ω) was significantly greater than that of blood pool (67.6 Ω; P<0.01). Models that incorporate LI drop (ΔLI) to predict lesion size had better performance than models that incorporate force-time integral (R2=0.75 versus R2=0.54) and generator impedance drop (R2=0.82 versus R2=0.58). Steam pops displayed a significantly higher starting LI and larger ΔLI compared with successful radiofrequency applications (P<0.01). CONCLUSIONS: LI recorded from miniature electrodes provides a valuable measure of catheter-tissue coupling, and ΔLI is predictive of lesion formation during radiofrequency ablation.

Diagnosis and management of atrial fibrillation: an overview.

Atrial fibrillation (AF) has been defined as the new cardiovascular "epidemic". Its prevalence is rising in developed countries, and the associated social and economic costs are high. In the last few years, there has been an increasing interest in understanding the mechanisms of AF and its management. New pharmacotherapies together with novel techniques for surgical and catheter treatment of AF have been developed, allowing the maintenance of sinus rhythm and the alleviation of symptoms in a large number of patients with AF. However, there are still some challenges that need to be addressed. This article gives an overview of the current state of the art on novel techniques for diagnosis and management of AF.

Precordial electrode placement for optimal ECG monitoring: implications for ambulatory monitor devices and event recorders.

INTRODUCTION: Detection of QRS complexes, P-waves and atrial fibrillation f-waves in electrocardiographic (ECG) signals is critical for the correct diagnosis of arrhythmias. We aimed to find the best bipolar lead (BL) with the highest signal amplitude and shortest inter-electrode spacing. METHODS: ECG signals (120 seconds) were recorded in 36 patients with 16 precordial electrodes placed in a standardized pattern. An average signal was analysed for each of 120 possible BLs obtained by calculating the difference between pairs of unipolar leads. Peak-to-peak amplitudes of QRS waves (50ms around R-peak) and P waves (270-70ms before R-peak) were calculated. For patients with atrial fibrillation, power of the fibrillatory (f) wave was used instead. Maximum values at each distance were considered and differentiation analysis was performed based on incremental changes (amplitude to distance). RESULTS: There was a significant correlation between distance and QRS-amplitude (r=0.78, p<0.001), P-wave amplitude (r=0.60, p<0.01) and f-wave power (r=0.79, p<0.001). The range of values was: QRS-amplitude 0.7-2.33mV, P-wave amplitude 0.07-0.18mV, and f-wave power 0.55-2.12mV(2)/s. The maximum value for the shortest distance was on a heart-aligned axis over the left ventricle for the QRS complex (1.9mV at 8.7cm) and over the atria for the P-wave (0.98mV) and f-waves (1.45mV(2)/s at 8cm, respectively). CONCLUSION: There is a strong positive correlation between electrode distance and ECG signal-amplitude. Distance of 8cm on a heart-aligned axis and over the relevant heart-chamber provides the highest signal amplitude for the shortest distance. These findings are essential for the design and use of ambulatory monitoring devices.

Ambulatory electrocardiology.

About 50 years ago, Norman Jefferis Holter invented a device that opened the possibility of recording heart activity over long periods of time. This invention, together with the rapid developments in electronics, has enabled a revolutionary change in the diagnosis and management of cardiac diseases. Ambulatory cardiac monitors have decreased in size to the point of becoming wearable or implantable and are able to monitor heart activity for months or even years. In addition, new telecommunication systems allow clinicians to remotely access cardiac events and to respond within a short period of time. Novel advances in computing and algorithm development are expanding the clinical applications of ambulatory devices with more complex automatic interpretation of the electrocardiographic signal. This article reviews the state of the art of these techniques from both clinical and technical approaches, covering a historic perspective up to today, and discusses current applications, challenges, and future directions.

An optimized DSP implementation of adaptive filtering and ICA for motion artifact reduction in ambulatory ECG monitoring.

Noise from motion artifacts is currently one of the main challenges in the field of ambulatory ECG recording. To address this problem, we propose the use of two different approaches. First, an adaptive filter with electrode-skin impedance as a reference signal is described. Secondly, a multi-channel ECG algorithm based on Independent Component Analysis is introduced. Both algorithms have been designed and further optimized for real-time work embedded in a dedicated Digital Signal Processor. We show that both algorithms improve the performance of a beat detection algorithm when applied in high noise conditions. In addition, an efficient way of choosing this methods is suggested with the aim of reduce the overall total system power consumption.

Frequency analysis of atrial fibrillation surface and intracardiac electrograms during pulmonary vein isolation.

AIMS: Frequency analysis of atrial electrograms from patients diagnosed with persistent atrial fibrillation (AF) appears to be crucial in its clinical diagnosis. This work explores the fibrillatory frequency properties of both surface and intracardiac electrograms before and after pulmonary vein isolation (PVI) using three time-frequency techniques. METHODS AND RESULTS: Surface electrocardiograms (ECGs) of 21 patients diagnosed with persistent AF undergoing PVI were recorded. Three methods, Fourier, ensemble average, and wavelet analysis, were used to identify the dominant frequency (DF) in surface ECGs. Dominant frequency was also computed in electrograms recorded within the coronary sinus (CS). Dominant frequency measured within the CS was best estimated in surface lead V1 using both Fourier (relative error: 10.94 ± 10.37%, correlation: 0.58) and wavelet analysis (relative error: 10.97 ± 11.08%, correlation: 0.53). Ensemble average gave highest relative error (21.29 ± 18.07%) and lowest correlation (0.10). Dominant frequency decreased after right PVI. This decrease was significant (P< 0.05) in most of the patients (13, 14, and 7 out of 14 when Fourier, wavelets, and ensemble average was used; 14 in CS). Further isolation of the left pulmonary veins (PVs) yielded a significant (P< 0.05) decrease in only a few of them (3, 4, and 2 out of 14 when Fourier, wavelets, and ensemble average was used; 4 in CS). CONCLUSION: Wavelet and Fourier analysis are good tools for estimating the atrial fibrillatory rate from surface ECG. A drop was observed in the DF value after isolation of the right PV. However, after left PVI this decrease was smaller.

Robust beat detector for ambulatory cardiac monitoring.

Robust beat detection under noisy conditions is required in order to obtain a correct clinical interpretation of the ECG in ambulatory settings. This paper describes the evaluation and optimization of a beat detection algorithm that is robust against high levels of noise. An evaluation protocol is defined in order to study four different characteristics of the algorithm: non-rhythmic patterns, different levels of SNR, exact peak detection and different levels of physical activity. This protocol is based on the MIT/BIH arrhythmia database and additional ECG recordings obtained under different levels of physical activity measured by 2-axis accelerometers. The optimized algorithm obtained a Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database while keeping a good performance on ECGs with high levels of activity (overall of Se=99.86%, +P=99.91%). In addition, this method was optimized to work in real time, for future implementation in a Wireless ECG sensor based on a microprocessor.

T-wave alternans found in preventricular tachyarrhythmias in CCU patients using a wavelet transform-based methodology.

Ventricular tachyarrhythmias are potentially lethal cardiac pathologies and the commonest cause of sudden cardiac death. Efforts to predict the onset of such events are based on feature extraction from the surface ECG. T-wave alternans (TWAs) are considered a marker of abnormal ventricular function that may be associated with ventricular tachycardia (VT) and ventricular fibrillation. A novel TWA detection algorithm utilizing the continuous wavelet transform is described in this paper. Simulated ECGs containing artificial TWA were used to test the algorithm that achieved a sensitivity of 91.40% and a specificity of 94.00%. The algorithm was subsequently used to analyze the ECGs of eight patients prior to the onset of VT. Of these, the algorithm indicated that five patients exhibited TWA prior to the onset of the tachyarrhythmic events, while the remaining three patients did not exhibit identifiable TWA. Healthy individuals were also studied in which one short TWA episode was detected by the algorithm. However, closer visual inspection of the data revealed this to be a likely false positive result.

Study of surface electrocardiogram spectrum before and after pulmonary vein ablation in patients with persistent atrial fibrillation.

Atrial fibrillation (AF) is the most common clinical cardiac arrhythmia and is usually treated with the electrical isolation of the pulmonary veins from the atria. However, it is estimated that about 30% of the patients undergoing this therapy will develop AF again. The purpose of this study was to test the dynamic changes of the spectra calculated in surface electrocardiograms (ECGs) before and after the ablation of the pulmonary veins. Surface ECGs of 14 patients with persistent AF undergoing this intervention were considered for this study. The QRS-T waves were subtracted from the ECGs using common spatial pattern, isolating the electrical activity of the atrium. The spectrum was then calculated and the main frequency peaks were identified. The smaller peaks with amplitude below 50% of the maximum were discarded. Eleven of the patients were followed up after the intervention for 1 to 6 months. Two of the patients developed AF again; the other 9 remained in sinus rhythm. For most of the patients, the spectrum showed more order after the intervention. However, the main frequency did not experience a significant decrease in average (5.1 Hz [+/-1.3] to 4.9 Hz [+/-1.0]). Interestingly, the increment or decrease of the main frequency did not correlate with the recurrence of AF or not.