Acute kidney disease is common and associated with poor outcomes in patients with cirrhosis and acute kidney injury.

BACKGROUND & AIMS: Acute kidney disease (AKD) is the persistence of acute kidney injury (AKI) for up to 3 months, which is proposed to be the time-window where critical interventions can be initiated to alter downstream outcomes of AKI. In cirrhosis, AKD and its impact on outcomes have been scantly investigated. We aimed to define the incidence and outcomes associated with AKD in a nationwide US cohort of hospitalized patients with cirrhosis and AKI. METHODS: Hospitalized patients with cirrhosis and AKI in the Cerner-Health-Facts database from 1/2009-09/2017 (n = 6,250) were assessed for AKD and were followed-up for 180 days. AKI and AKD were defined based on KDIGO and ADQI AKD and renal recovery consensus criteria, respectively. The primary outcome measure was mortality, and the secondary outcome measure was de novo chronic kidney disease (CKD). Competing-risk multivariable models were used to determine the independent association of AKD with primary and secondary outcomes. RESULTS: AKD developed in 32% of our cohort. On multivariable competing-risk analysis adjusting for significant confounders, patients with AKD had higher risk of mortality at 90 (subdistribution hazard ratio [sHR] 1.37; 95% CI 1.14-1.66; p = 0.001) and 180 (sHR 1.37; 95% CI 1.14-1.64; p = 0.001) days. The incidence of de novo CKD was 37.5%: patients with AKD had higher rates of de novo CKD (64.0%) compared to patients without AKD (30.7%; p <0.001). After adjusting for confounders, AKD was independently associated with de novo CKD (sHR 2.52; 95% CI 2.01-3.15; p <0.001) on multivariable competing-risk analysis. CONCLUSIONS: AKD develops in 1 in 3 hospitalized patients with cirrhosis and AKI and it is associated with worse survival and de novo CKD. Interventions that target AKD may improve outcomes of patients with cirrhosis and AKI. LAY SUMMARY: In a nationwide US cohort of hospitalized patients with cirrhosis and acute kidney injury, acute kidney disease developed in 1 in 3 patients and was associated with worse survival and chronic kidney disease. Interventions that target acute kidney disease may improve outcomes of patients with cirrhosis and acute kidney injury.

Practice patterns and outcomes associated with intravenous albumin in patients with cirrhosis and acute kidney injury.

BACKGROUND & AIMS: Guidelines recommend albumin as the plasma-expander of choice for acute kidney injury (AKI) in cirrhosis. However, the impact of these recommendations on patient outcomes remains unclear. We aimed to determine the practice-patterns and outcomes associated with albumin use in a large, nationwide-US cohort of hospitalized cirrhotics with AKI. METHODS: A retrospective cohort study was performed in hospitalized cirrhotics with AKI using Cerner-Health-Facts database from January 2009 to March 2018. 6786 were included for analysis on albumin-practice-patterns, and 4126 had available outcomes data. Propensity-score-adjusted model was used to determine the association between albumin use, AKI-recovery and in-hospital survival. RESULTS: Median age was 61-years (60% male, 70% white), median serum-creatinine was 1.8 mg/dL and median Model for End-stage Liver Disease Sodium (MELD-Na) score was 24. Albumin was given to 35% of patients, of which 50% received albumin within 48-hours of AKI-onset, and 17% received appropriate weight-based dosing. Albumin was used more frequently in patients with advanced complications of cirrhosis, higher MELD-Na scores and patients admitted to urban-teaching hospitals. After propensity-matching and multivariable adjustment, albumin use was not associated with AKI-recovery (odds ratio [OR] 0.70, 95% confidence-interval [CI]: 0.59-1.07, P = .130) or in-hospital survival (OR 0.76 [95% CI: 0.46-1.25], P = .280), compared with crystalloids. Findings were unchanged in subgroup analyses of patients with varying cirrhosis complications and disease severity. CONCLUSIONS: USA hospitalized patients with cirrhosis and AKI frequently do not receive intravenous albumin, and albumin use was not associated with improved clinical outcomes. Prospective randomised trials are direly needed to evaluate the impact of albumin in cirrhotics with AKI.

Using CUSUM in real time to signal clinically relevant decreases in estimated glomerular filtration rate.

BACKGROUND: The electronic health record (EHR), utilized to apply statistical methodology, assists provider decision-making, including during the care of chronic kidney disease (CKD) patients. When estimated glomerular filtration (eGFR) decreases, the rate of that change adds meaning to a patient's single eGFR and may represent severity of renal injury. Since the cumulative sum chart technique (CUSUM), often used in quality control and surveillance, continuously checks for change in a series of measurements, we selected this statistical tool to detect clinically relevant eGFR decreases and developed CUSUMGFR. METHODS: In a retrospective analysis we applied an age adjusted CUSUMGFR, to signal identification of eventual ESKD patients prior to diagnosis date. When the patient signaled by reaching a specified threshold value, days from CUSUM signal date to ESKD diagnosis date (earliness days) were measured, along with the corresponding eGFR measurement at the signal. RESULTS: Signaling occurred by CUSUMGFR on average 791 days (se = 12 days) prior to ESKD diagnosis date with sensitivity = 0.897, specificity = 0.877, and accuracy = .878. Mean days prior to ESKD diagnosis were significantly greater in Black patients (905 days) and patients with hypertension (852 days), diabetes (940 days), cardiovascular disease (1027 days), and hypercholesterolemia (971 days). Sensitivity and specificity did not vary by sociodemographic and clinical risk factors. CONCLUSIONS: CUSUMGFR correctly identified 30.6% of CKD patients destined for ESKD when eGFR was > 60 ml/min/1.73 m2 and signaled 12.3% of patients that did not go on to ESKD (though almost all went on to later-stage CKD). If utilized in an EHR, signaling patients could focus providers' efforts to slow or prevent progression to later stage CKD and ESKD.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU.

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity.

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

Identifying and analyzing sepsis states: A retrospective study on patients with sepsis in ICUs.

Sepsis accounts for more than 50% of hospital deaths, and the associated cost ranks the highest among hospital admissions in the US. Improved understanding of disease states, progression, severity, and clinical markers has the potential to significantly improve patient outcomes and reduce cost. We develop a computational framework that identifies disease states in sepsis and models disease progression using clinical variables and samples in the MIMIC-III database. We identify six distinct patient states in sepsis, each associated with different manifestations of organ dysfunction. We find that patients in different sepsis states are statistically significantly composed of distinct populations with disparate demographic and comorbidity profiles. Our progression model accurately characterizes the severity level of each pathological trajectory and identifies significant changes in clinical variables and treatment actions during sepsis state transitions. Collectively, our framework provides a holistic view of sepsis, and our findings provide the basis for future development of clinical trials, prevention, and therapeutic strategies for sepsis.

Adherence to anticoagulant guideline for atrial fibrillation: A large care gap among stroke patients in a rural population.

OBJECTIVE: This study aimed to investigate the prevalence and factors associated with oral anticoagulant undertreatment of atrial fibrillation (AF) among a cohort of rural patients with stroke outcomes and examine how undertreatment may influence a patient's one-year survival after stroke. METHODS: This retrospective cohort study examined ischemic stroke patients with pre-stroke AF diagnosis from September 2003 to May 2019 and divided them into proper treatment and undertreatment group. Analysis included chi-square test, variance analysis, Kruskal-Wallis test, logistic regression, Kaplan-Meier estimator, and Cox proportional-hazards model. RESULTS: Out of 1062 ischemic stroke patients with a pre-stroke AF diagnosis, 1015 patients had a CHA2DS2-VASc score ≥2, and 532 (52.4%) of those were undertreated. Median time from AF diagnosis to index stroke was significantly lower among undertreated patients (1.9 years vs. 3.6 years, p < 0.001). Other thromboembolism, excluding stroke, TIA, and myocardial infarction (OR 0.41, p < 0.001), the number of encounters per year (OR 0.90, p < 0.001), and the median time between AF diagnosis and stroke event (OR 0.86, p < 0.001) were negatively associated with undertreatment. Kaplan-Meier estimator showed no statistical difference in the one-year survival probability between groups (log-rank test, p = 0.29), while the Cox-Hazard model showed that age (HR 1.05, p < 0.001) and history of congestive heart failure (HR 1.88, p < 0.001) increased the risk of mortality. CONCLUSIONS: More than half of our rural stroke patients with a pre-index AF diagnosis were not on guideline-recommended treatment. The study highlights a large care gap and an opportunity to improve AF management.

Fast-Track Long Term Continuous Heart Monitoring in a Stroke Clinic: A Feasibility Study.

Background: Paroxysmal atrial fibrillation (PAF) or flutter is prevalent among patients with cryptogenic stroke. The goal of this study was to investigate the feasibility of incorporating a fast-track, long term continuous heart monitoring (LTCM) program within a stroke clinic. Method: We designed and implemented a fast-track LTCM program in our stroke clinics. The instrument that we used for the study was the ZioXT® device from IRhythm™ Technologies. To implement the program, all clinic support staff received training on the skin preparation and proper placement of the device. We prospectively followed every patient who had a request from one of our inpatient or outpatient stroke or neurology providers to receive LTCM. We recorded patients' demographics, the LTCM indication, as well as related quality measures including same-visit placement, wearing time, analyzable time, LTCM application to the preliminary finding time, as well as patients' out of pocket cost. Results: Out of 501 patients included in the study, 467 (93.2%) patients (mean age 65.9 ± 13; men: 48%) received LTCM; and 92.5% of the patients had the diagnosis of stroke or TIA. 93.7% of patients received their LTCM during the same outpatient visit in the stroke clinic. The mean wearing time for LTCM was 12.1 days (out of 14 days). The average analyzable time among our patients was 95.0%. Eighteen (3.9%, 95%CI: 2.4-6.0) patients had at least one episode of PAF that was sustained for more than 30 s. The rate of PAF was 5.9% (95% CI: 3.5-9.2) among patients with the diagnosis of stroke. Out of 467 patients, 392 (84%) had an out-of-pocket cost of < $100. Conclusion: It is feasible to implement a fast-track cardiac monitoring as part of a stroke clinic with proper training of stroke providers, clinic staff, and support from a cardiology team.

Classification of short single-lead electrocardiograms (ECGs) for atrial fibrillation detection using piecewise linear spline and XGBoost.

OBJECTIVE: Detection of atrial fibrillation is important for risk stratification of stroke. We developed a novel methodology to classify electrocardiograms (ECGs) to normal, atrial fibrillation and other cardiac dysrhythmias as defined by the PhysioNet Challenge 2017. APPROACH: More specifically, we used piecewise linear splines for the feature selection and a gradient boosting algorithm for the classifier. In the algorithm, the ECG waveform is fitted by a piecewise linear spline, and morphological features relating to the piecewise linear spline coefficients are extracted. XGBoost is used to classify the morphological coefficients and heart rate variability features. MAIN RESULTS: The performance of the algorithm was evaluated by the PhysioNet Challenge database (3658 ECGs classified by experts). Our algorithm achieved an average F 1 score of 81% for a 10-fold cross-validation and also achieved 81% for F 1 score on the independent testing set. This score is similar to the top 9th score (81%) in the official phase of the PhysioNet Challenge 2017. SIGNIFICANCE: Our algorithm presents a good performance on multi-label short ECG classification with selected morphological features.

Big data in healthcare - the promises, challenges and opportunities from a research perspective: A case study with a model database.

Recent advances in data collection during routine health care in the form of Electronic Health Records (EHR), medical device data (e.g., infusion pump informatics, physiological monitoring data, and insurance claims data, among others, as well as biological and experimental data, have created tremendous opportunities for biological discoveries for clinical application. However, even with all the advancement in technologies and their promises for discoveries, very few research findings have been translated to clinical knowledge, or more importantly, to clinical practice. In this paper, we identify and present the initial work addressing the relevant challenges in three broad categories: data, accessibility, and translation. These issues are discussed in the context of a widely used detailed database from an intensive care unit, Medical Information Mart for Intensive Care (MIMIC III) database.

Symptom Levels in Care-Seeking Bangladeshi and Nepalese Adults With Advanced Cancer.

PURPOSE: Three-fourths of patients with advanced cancer are reported to suffer from pain. A primary barrier to provision of adequate symptom treatment is failure to appreciate the intensity of the symptoms patients are experiencing. Because data on Bangladeshi and Nepalese patients' perceptions of their symptomatic status are limited, we sought such information using a cell phone questionnaire. METHODS: At tertiary care centers in Dhaka and Kathmandu, we recruited 640 and 383 adult patients, respectively, with incurable malignancy presenting for outpatient visits and instructed them for that single visit on one-time completion of a cell phone platform 15-item survey of questions about common cancer-associated symptoms and their magnitudes using Likert scales of 0 to 10. The questions were taken from the Edmonton Symptom Assessment System and the Brief Pain Inventory instruments. RESULTS: All but two Bangladeshi patients recruited agreed to study participation. Two-thirds of Bangladeshi patients reported usual pain levels ≥ 5, and 50% of Nepalese patients reported usual pain levels ≥ 4 (population differences significant at P < .001). CONCLUSION: Bangladeshi and Nepalese adults with advanced cancer are comfortable with cell phone questionnaires about their symptoms and report high levels of pain. Greater attention to the suffering of these patients is warranted.

Closing the Data Loop: An Integrated Open Access Analysis Platform for the MIMIC Database.

We describe a new model for collaborative access, exploration, and analyses of the Medical Information Mart for Intensive Care - III (MIMIC III) database for translational clinical research. The proposed model addresses the significant disconnect between data collection at the point of care and translational clinical research. It addresses problems of data integration, preprocessing, normalization, analyses (along with associated compute back-end), and visualization. The proposed platform is general, and can be easily adapted to other databases. The pre-packaged analyses toolkit is easily extensible, and allows for multi-language support. The platform can be easily federated, mirrored at other locations, and supports a RESTful API for service composition and scaling.

The mixing rate of the arterial blood pressure waveform Markov chain is correlated with shock index during hemorrhage in anesthetized swine.

Identifying the need for interventions during hemorrhage is complicated due to physiological compensation mechanisms that can stabilize vital signs until a significant amount of blood loss. Physiological systems providing compensation during hemorrhage affect the arterial blood pressure waveform through changes in dynamics and waveform morphology. We investigated the use of Markov chain analysis of the arterial blood pressure waveform to monitor physiological systems changes during hemorrhage. Continuous arterial blood pressure recordings were made on anesthetized swine (N=7) during a 5 min baseline period and during a slow hemorrhage (10 ml/kg over 30 min). Markov chain analysis was applied to 20 sec arterial blood pressure waveform segments with a sliding window. 20 ranges of arterial blood pressure were defined as states and empirical transition probability matrices were determined for each 20 sec segment. The mixing rate (2(nd) largest eigenvalue of the transition probability matrix) was determined for all segments. A change in the mixing rate from baseline estimates was identified during hemorrhage for each animal (median time of 13 min, ~10% estimated blood volume, with minimum and maximum times of 2 and 33 min, respectively). The mixing rate was found to have an inverse correlation with shock index for all 7 animals (median correlation coefficient of -0.95 with minimum and maximum of -0.98 and -0.58, respectively). The Markov chain mixing rate of arterial blood pressure recordings is a novel potential biomarker for monitoring and understanding physiological systems during hemorrhage.