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Competing Interests: The authors declare no conflicts of interest.

Background: Recorded at the time of transplant and reported to the Organ Procurement and Transplantation Network, patient's functional status is measured using the Karnofsky performance score (KPS), ranging 0 to 100. Functional status analysis may provide insights on candidate listing and posttransplant survival outcomes for deceased-donor kidney transplants., Methods: The cohort consisted of adult deceased-donor kidney transplant recipients transplanted beginning January 2007. One-year and 3-year Cox models for posttransplant survival were fitted with current Scientific Registry of Transplant Recipients (SRTR) variables and KPS. Comparative analyses were performed between the SRTR model without KPS and augmented model with it. Using the augmented model, we examined the impact of Kidney Donor Profile Index on posttransplant survivals for 5 different KPS strata: 10 to 30, 40 to 50, 60 to 70, 80 to 90, and 100., Results: Comparative analyses showed that KPS was a statistically significant predictor for posttransplant survival: it improved model calibration, discrimination, and predictive accuracy. From the augmented model, the survival curves illustrated that recipients with KPS 40 to 50 and kidneys with Kidney Donor Profile Index as high as 99 have expected survival probabilities of above 90% in 1 year and above 80% in 3 years. The expected survival probabilities improve as KPS increases. Recipients with KPS 10 to 30 have the worst survival probability, even if they received high-quality kidneys., Conclusions: Insights from the survival analyses recommend possible inclusion of functional status into SRTR's risk-adjusted models. Moreover, they invite further examination of its use to improve current listing and transplantation strategies at transplant centers and potentially reduce deceased-donor kidney discard rate.

Background: Underutilization of marginal-quality kidneys for transplantation produced ideas of expediting kidney placement for populations with decreased opportunities of receiving transplants. Such policies can be less efficacious for specific individuals and should be scrutinized until the decision-making for accepting marginal-quality organs, which has relied on experiential judgment, is better understood at the individual level. There exist rigorous tools promoting personalized decisions with useful and objective information., Methods: This article introduces a decision-tree methodology that analyzes a patient's dilemma: to accept a kidney offer now or reject it. The methodology calculates the survival benefit of accepting a kidney given a certain quality now and the survival benefit of rejecting it. Survival benefit calculation accounts for patients' and donors' characteristics and transplant centers' and organ procurement organizations' performances and incorporates patients' perceived transplant and dialysis utilities. Valuations of rejecting an offer are contingent on future opportunities and subject to uncertainty in the timing of successive kidney offers and their quality and donor characteristics., Results: The decision tree was applied to a realistic patient profile as a demonstration. The tool was tested on 1000 deceased-donor kidney offers in 2016. Evaluating up to 1 year of future offers, the tool attains 61% accuracy, with transplant utility of 1.0 and dialysis utility of 0.5. The accuracy reveals potential bias in kidney offer acceptance/rejection at transplant centers., Conclusions: The decision-tree tool presented could aid personalized transplant decision-making in the future by providing patients with calculated, individualized survival benefits between accepting and rejecting a kidney offer.

Background: Policymakers are deliberating reforms to reduce geographic disparity in liver allocation. Public comments and the United Network for Organ Sharing Liver and Intestinal Committee have expressed interest in refining the neighborhoods approach. Share 35 and Share 15 policies affect geographic disparity., Methods: We construct concentric neighborhoods superimposing the current 11 regions. Using concepts from concentric circles, we construct neighborhoods for each donor service area (DSA) that consider all DSAs within 400, 500, or 600 miles as neighbors. We consider limiting each neighborhood to 10 DSAs and use no metrics for liver supplies and demands. We change Model for End-Stage Liver Disease (MELD) thresholds for the Share 15 policy to 18 or 20 and apply 3- and 5-point MELD proximity boosts to enhance local priority, control travel distances, and reduce disparity. We conduct simulations comparing current allocation with the neighborhoods and sharing policies., Results: Concentric neighborhoods structures provide an array of solutions where simulation results indicate that they reduce geographic disparity, annual mortalities, and the airplane travel distances by varying degrees. Tuning of the parameters and policy combinations can lead to beneficial improvements with acceptable transplant volume loss and reductions in geographic disparity and travel distance. Particularly, the 10-DSA, 500-mile neighborhood solution with Share 35, Share 15, and 0-point MELD boost achieves such while limiting transplant volume losses to below 10%., Conclusions: The current 11 districts can be adapted systematically by adding neighboring DSAs to improve geographic disparity, mortality, and airplane travel distance. Modifications to Share 35 and Share 15 policies result in further improvements. The solutions may be refined further for implementation.

Objectives: Cardiac biomarkers have been studied in sepsis in the past and various mechanisms for their rise have been elucidated. However their association with severity of sepsis, mortality and myocardial dysfunction warrants further studies. We have studied three different cardiac biomarkers- troponin T (trop T), creatine phosphokinase MB isoform (CPK MB) and NT pro brain natriuretic peptide (NT Pro BNP) in patients with septicemia. We have attempted to observe the levels of these biomarkers in sepsis, their individual abilities to predict the severity of sepsis, mortality and association with myocardial dysfunction noted in echocardiography., Results: There were 54 patients each of septicaemia and controls. The means of the three biomarkers, namely Troponin T, CPK MB and NT Pro BNP, were significantly elevated in patients with sepsis- mean values of 0.23±0.8 ng/ml, 9.9±13.4 ng/ml and 5988.62±13.7 pg/ml respectively. Myocardial dysfunction was observed in 27 cases. There were 13 non-survivors. Troponin T and NT pro BNP were strongly associated with higher mortality. CPK MB had better correlation with myocardial dysfunction., Conclusion: We conclude that myocardial dysfunction using echocardiography is seen in around half of the patients with sepsis. Cardiac biomarkers can be routinely used in patients of septicemia to suggest the severity of sepsis,to detect myocardial injury and dysfunction and prognostication. CPK MB may be very useful to suspect myocardial dysfunction in such patients.

Background: Geographic disparities persist in access to liver transplantation. Candidates with similar urgency experience varying opportunities for transplants across the United States. Policymakers are poised to act and 1 proposal entails reorganizing the current Organ Procurement and Transplant Network (OPTN) of 11 regions into 8 districts. However, redistricting has the shortcomings that Organ Procurement Organizations (OPOs) are disconnected from their immediate neighbors by district borders and that it is not easily responsive to uncertainty resulting from variability in donor and listing rates., Methods: We introduce the notion of an OPO's neighborhood-a collection of donor service areas (DSA) surrounding the OPO that acts as the OPO's region in the current local-regional-national framework. Districts and concentric circles are special cases. We design 58 neighborhoods for the DSAs with several attractive properties and optimize them to balance supplies and demands using 10 years of Organ Procurement and Transplant Network data. We conduct a simulation experiment comparing current allocation, redistricting, and neighborhoods under current sharing policies with respect to the following metrics: total mortalities, DSA-average model for end-stage liver disease (MELD) at transplant, DSA-average MELD standard deviation, and average organ transport distance. Liver-simulated allocation model cannot accommodate neighborhoods, so we programmed a discrete-event simulator, LivSim, to approximate liver-simulated allocation model., Results: We exhibited a neighborhood solution. Compared with the current allocation, simulation results showed that neighborhoods reduce the DSA-average MELD standard deviation by 29% and save about 65 lives annually. Compared with redistricting, the neighborhoods had smaller average transport distances that were more uniform across DSAs, saved about 20 additional lives, and reduced DSA-average MELD standard deviation by an additional 17%., Conclusions: Alternatives to redistricting with desirable properties and performance are possible and should be considered.

This paper describes a system using Kinect sensor to detect patient walk automatically in a hospital room setting. The system is especially essential for the case when the patient is alone and the nursing staff is absent. The patient activities are represented by the features extracted from Kinect V2 skeletons. The analysis to the recognized walk could help us to better understand the health situation of the patient and the possible hospital acquired infection (HAI), and provide valuable information to healthcare givers for making a corresponding treatment decision and alteration. The Kinect V2 depth sensor provides the ground truth.

Background and Objective: Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time consuming, which limits the number of chart reviews in a large database. The authors' objective is to develop a new design-of-experiments-based systematic chart validation and review (DSCVR) approach that is more powerful than the random validation sampling used in existing approaches., Methods: The DSCVR approach judiciously and efficiently selects the cases to validate (i.e., validate whether the response values are correct for those cases) for maximum information content, based only on their predictor variable values. The final predictive model will be fit using only the validation sample, ignoring the remainder of the unvalidated and unreliable error-prone data. A Fisher information based D-optimality criterion is used, and an algorithm for optimizing it is developed., Results: The authors' method is tested in a simulation comparison that is based on a sudden cardiac arrest case study with 23 041 patients' records. This DSCVR approach, using the Fisher information based D-optimality criterion, results in a fitted model with much better predictive performance, as measured by the receiver operating characteristic curve and the accuracy in predicting whether a patient will experience the event, than a model fitted using a random validation sample., Conclusions: The simulation comparisons demonstrate that this DSCVR approach can produce predictive models that are significantly better than those produced from random validation sampling, especially when the event rate is low., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Complexity in clinical workflows can lead to inefficiency in making diagnoses, ineffectiveness of treatment plans and uninformed management of healthcare organizations (HCOs). Traditional strategies to manage workflow complexity are based on measuring the gaps between workflows defined by HCO administrators and the actual processes followed by staff in the clinic. However, existing methods tend to neglect the influences of EMR systems on the utilization of workflows, which could be leveraged to optimize workflows facilitated through the EMR. In this paper, we introduce a framework to infer clinical workflows through the utilization of an EMR and show how such workflows roughly partition into four types according to their efficiency. Our framework infers workflows at several levels of granularity through data mining technologies. We study four months of EMR event logs from a large medical center, including 16,569 inpatient stays, and illustrate that over approximately 95% of workflows are efficient and that 80% of patients are on such workflows. At the same time, we show that the remaining 5% of workflows may be inefficient due to a variety of factors, such as complex patients.

Patients spend the vast majority of their hospital stay in an unmonitored bed where various mobility factors can impact patient safety and quality. Specifically, bed positioning and a patient's related mobility in that bed can have a profound impact on risks such as pneumonias, blood clots, bed ulcers and falls. This issue has been exacerbated as the nurse-per-bed (NPB) ratio has decreased in recent years. To help assess these risks, it is critical to monitor a hospital bed's positional status (BPS). Two bed positional statuses, bed height (BH) and bed chair angle (BCA), are of critical interests for bed monitoring. In this paper, we develop a bed positional status detection system using a single Microsoft Kinect. Experimental results show that we are able to achieve 94.5% and 93.0% overall accuracy of the estimated BCA and BH in a simulated patient's room environment.

Background: Risk stratification of patients with acute myocardial infarction is based on various clinical, biochemical or electrocardiographic parameters. There is emerging evidence that N-terminal probrain natriuretic peptides (NT-proBNP) possess characteristics of an ideal biomarker. In this study we looked into the role of NT-proBNP in risk stratification and prediction of short-term events in patients presenting with acute myocardial infarction (MI) and having preserved left ventricular functions as assessed by ejection fraction (EF) on echocardiography., Methods and Results: Of a total of 250 consecutive patients admitted with a diagnosis of acute ST segment elevation myocardial infarction, 84 patients were found to have ejection fraction greater than 50% (44 with anterior MI, 40 with inferior MI. Serum NT-proBNP was measured using electrochemiluminiscence assay (Roche). On two-dimensional echocardiography, modified Simpson's technique was used to measure the EF. Follow-up at day 30 included a two-dimensional echocardiography and assessment for worsening heart failure, recurrent ischemia, and repeat hospitalization. Death due to cardiovascular cause by 30 days was also noted. The mean value of NT-proBNP for those having EF over 50% was 1542.38 + 4649.12 pg/ml. For the purpose of a dichotomous analysis, the median value was determined (907.5 pg/ml). In patients having NT-proBNP above median, the Killip class was expectedly higher 1.62 + 0.21 vs 1.0 + 0.12 ( p< 0.05) and the thrombolysis in myocardial infarction scores were worse (4.77 + 1.56 vs 2.71 + 1.11, p < 0.05). The ejection fraction was similar (59.72 + 8.8 vs 58.76 + 6.9, p= NS) in the two groups. At 30 days followup, patients having NT-proBNP above median showed a further decline in the Killip class and EF. The clinical outcomes (composite of recurrent ischemia, worsening heart failure and repeat hospitalization) were also worse in this group ( p< 0.05)., Conclusion: In patients with apparently normal ejection fraction and without left ventricular dysfunction, a higher NT-proBNP level would suggest poorer short-term clinical outcomes and would require a more aggressive treatment strategy.

Background: Risk stratification and prediction of high risk for mortality in patients with acute coronary syndromes is based on clinical evaluation, electrocardiogram, biochemical markers and various risk assessment scores. There is emerging evidence that N-terminal probrain natriuretic peptide possesses several characteristics of an ideal biomarker. In this study we looked into the role of N-terminal probrain natriuretic peptide in risk stratification and prediction of short-term events including mortality in patients presenting with acute coronary syndrome., Methods and Results: A total of 120 consecutive patients admitted with a diagnosis of acute myocardial infarction, including both ST elevation myocardial infarction (n=80) and non-ST elevation myocardial infarction (n=40) were enrolled. Serum N-terminal probrain natriuretic peptide was measured using electrochemiluminiscence assay (Roche Diagnostics), on the Elecsys 2010 system. On two-dimentional echocardiography, modified Simpson's technique was used to measure the ejection fraction along with end-systolic volume. Various other demographic variables, echocardiographic parameters and risk scores were also assessed. Follow-up at day 30 included a two-dimentional echocardiographic evaluation and assessment for worsening heart failure, recurrent ischemia, and repeat hospitalization. Death due to cardiovascular cause by 30 days was also noted. The mean value of N-terminal probrain natriuretic peptide for the whole cohort was 2307 +/- 2287 pg/ml (271.4 +/- 269.1 pmol/L). For the purpose of comparative analysis, the median value was determined [1403 pg/ml (165 pmol/L)]. In patients having N-terminal probrain natriuretic peptide above median, the end-systolic volume was higher while ejection fraction was significantly lower at baseline (p<0.05). At 30 days follow-up, there was a further decline in ejection fraction from 47.7 +/- 11.4 to 43.9 +/- 9.9 (p<0.05), and clinical outcomes were worse in this group. There was a 5% mortality in the entire study group and all patients who died had N-terminal probrain natriuretic peptide above median. On multivariate logistic regression analysis, N-terminal probrain natriuretic peptide above median (OR=32.79, 95% CI 8.74-123.1, p<0.001) emerged as the strongest predictors of adverse outcomes, including 30-day mortality (p<0.001)., Conclusions: N-terminal probrain natriuretic peptide emerged as a strong prognostic tool across the spectrum of acute myocardial infarction and had the strongest predictive value for short-term adverse outcomes including death.

Background: Auscultation of the third heart sound is an age-old sign for predicting ventricular dysfunction. New technology and biomarkers like two-dimensional echocardiography and N-terminal pro brain natriuretic peptide, respectively, have sidelined the utility of this sign, which does not involve any cost and is readily accessible. We sought to find the predictive accuracy of third heart sound and its correlation with N-terminal pro brain natriuretic peptide and ejection fraction using two-dimensional echocardiography to detect left ventricular dysfunction in patients of acute coronary syndrome., Methods and Results: One hundred and ten patients presenting with acute coronary syndrome [acute ST elevation myocardial infarction (n=74) and non-ST elevation myocardial infarction (n=36)] were prospectively studied. A senior cardiologist, blinded to N-terminal pro brain natriuretic peptide and ejection fraction results auscultated for a left ventricular third heart sound in each patient. Ejection fraction was measured using modified Simpson's technique on two-dimensional echocardiography and N-terminal pro brain natriuretic peptide was measured using electrochemiluminiscence assay. Median levels of N-terminal pro brain natriuretic peptide were used to provide a dichotomous approach for analysis of the data. Third heart sound was present in 40 patients (acute ST elevation myocardial infarction: n=27, non-ST elevation myocardial infarction: n=13) and absent in 70 patients (acute ST elevation myocardial infarction: n=47, non-ST elevation myocardial infarction: n=23). The sensitivity and specificity of third heart sound for predicting N-terminal pro brain natriuretic peptide above median was 65.5% and 92.7%, respectively. The positive and negative predictive value was 90% and 73%, respectively. The N-terminal pro brain natriuretic peptide of those having third heart sound was 4081 +/- 2705 pg/ml compared to 1239.3 +/- 1169 pg/ml in those without third heart sound (p < 0.001). The sensitivity of third heart sound to detect ejection fraction <45% was 67.9% while the specificity was 74.4%. The positive and the negative predictive values were 47.5% and 87.1%, respectively. The ejection fraction of patients having third heart sound was 47.5 +/- 11.3% compared to 56 +/- 10.4% without third heart sound (p < 0.001)., Conclusions: Auscultation of third heart sound has a good specificity and predictive value for predicting elevated N-terminal pro brain natriuretic peptide and left ventricular dysfunction. Thus age-old clinical cardiology still holds its forte in this new era of technology-driven cardiology.