Predictors of mortality by an artificial intelligence enhanced electrocardiogram model for cardiac amyloidosis.

AIMS: We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). METHODS: A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild-type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan-Meier was used to estimate survival. RESULTS: Median age of the cohort was 67 [inter-quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two-fold risk of all-cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. CONCLUSIONS: Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.

Creating an atlas of normal tissue for pruning WSI patching through anomaly detection.

Patching whole slide images (WSIs) is an important task in computational pathology. While most of them are designed to classify or detect the presence of pathological lesions in a WSI, the confounding role and redundant nature of normal histology are generally overlooked. In this paper, we propose and validate the concept of an "atlas of normal tissue" solely using samples of WSIs obtained from normal biopsies. Such atlases can be employed to eliminate normal fragments of tissue samples and hence increase the representativeness of the remaining patches. We tested our proposed method by establishing a normal atlas using 107 normal skin WSIs and demonstrated how established search engines like Yottixel can be improved. We used 553 WSIs of cutaneous squamous cell carcinoma to demonstrate the advantage. We also validated our method applied to an external dataset of 451 breast WSIs. The number of selected WSI patches was reduced by 30% to 50% after utilizing the proposed normal atlas while maintaining the same indexing and search performance in leave-one-patient-out validation for both datasets. We show that the proposed concept of establishing and using a normal atlas shows promise for unsupervised selection of the most representative patches of the abnormal WSI patches.

Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus.

OBJECTIVE: Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. METHODS: Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. RESULTS: Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). CONCLUSIONS: An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs micrographic surgeon. Advances in artificial intelligence (AI) have made possible the development of sophisticated clinical decision support systems to provide real-time feedback to clinicians which could have a role in optimizing the diagnostic workup of BCC. There were 287 annotated whole-slide images of frozen sections from tangential biopsies, of which 121 contained BCC, that were used to train and test an AI pipeline to recognize BCC. Regions of interest were annotated by a senior dermatology resident, experienced dermatopathologist, and experienced Mohs surgeon, with concordance of annotations noted on final review. Final performance metrics included a sensitivity and specificity of 0.73 and 0.88, respectively. Our results on a relatively small dataset suggest the feasibility of developing an AI system to aid in the workup and management of BCC.

Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis.

BACKGROUND: We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA). OBJECTIVES: In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders. METHODS: Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups. RESULTS: The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively). CONCLUSIONS: The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.

Sequential organ failure assessment score improves survival prediction for left ventricular assist device recipients in intensive care.

BACKGROUND: Preoperative risk scores facilitate patient selection, but postoperative risk scores may offer valuable information for predicting outcomes. We hypothesized that the postoperative Sequential Organ Failure Assessment (SOFA) score would predict mortality after left ventricular assist device (LVAD) implantation. METHODS: We retrospectively reviewed data from 294 continuous-flow LVAD implantations performed at Mayo Clinic Rochester during 2007 to 2015. We calculated the EuroSCORE, HeartMate-II Risk Score, and RV Failure Risk Score from preoperative data and the APACHE III and Post Cardiac Surgery (POCAS) risk scores from postoperative data. Daily, maximum, and mean SOFA scores were calculated for the first 5 postoperative days. The area under receiver-operator characteristic curves (AUC) was calculated to compare the scoring systems' ability to predict 30-day, 90-day, and 1-year mortality. RESULTS: For the entire cohort, mortality was 5% at 30 days, 10% at 90 days, and 19% at 1 year. The Day 1 SOFA score had better discrimination for 30-day mortality (AUC 0.77) than the preoperative risk scores or the APACHE III and POCAS postoperative scores. The maximum SOFA score had the best discrimination for 30-day mortality (AUC 0.86), and the mean SOFA score had the best discrimination for 90-day mortality (AUC 0.82) and 1-year mortality (AUC 0.76). CONCLUSIONS: We observed that postoperative mean and maximum SOFA scores in LVAD recipients predict short-term and intermediate-term mortality better than preoperative risk scores do. However, because preoperative and postoperative risk scores each contribute unique information, they are best used in concert to predict outcomes after LVAD implantation.

Deep learning for dermatologists: Part I. Fundamental concepts.

Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.

Temporal Trends, Predictors and Outcomes of Inpatient Palliative Care Use in Cardiac Arrest Complicating Acute Myocardial Infarction.

BACKGROUND: Utilization of inpatient palliative care services (PCS) has been infrequently studied in patients with cardiac arrest complicating acute myocardial infarction (AMI-CA). METHODS: Adult AMI-CA admissions were identified from the National Inpatient Sample (2000-2017). Outcomes of interest included temporal trends and predictors of PCS use and in-hospital mortality, length of stay, hospitalization costs and discharge disposition in AMI-CA admissions with and without PCS use. Multivariable logistic regression and propensity matching were used to adjust for confounding. RESULTS: Among 584,263 AMI-CA admissions, 26,919 (4.6%) received inpatient PCS. From 2000 to 2017 PCS use increased from <1% to 11.5%. AMI-CA admissions that received PCS were on average older, had greater comorbidity, higher rates of cardiogenic shock, acute organ failure, lower rates of coronary angiography (48.6% vs 63.3%), percutaneous coronary intervention (37.4% vs 46.9%), and coronary artery bypass grafting (all p < 0.001). Older age, greater comorbidity burden and acute non-cardiac organ failure were predictive of PCS use. In-hospital mortality was significantly higher in the PCS cohort (multivariable logistic regression: 84.6% vs 42.9%, adjusted odds ratio 3.62 [95% CI 3.48-3.76]; propensity-matched analysis: 84.7% vs. 66.2%, p < 0.001). The PCS cohort received a do- not-resuscitate status more often (47.6% vs. 3.7%), had shorter hospital stays (4 vs 5 days), and were discharged more frequently to skilled nursing facilities (73.6% vs. 20.4%); all p < 0.001. These results were consistent in the propensity-matched analysis. CONCLUSIONS: Despite an increase in PCS use in AMI-CA, it remains significantly underutilized highlighting the role for further integrating of these specialists in AMI-CA care.

Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis.

OBJECTIVE: To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG). METHODS: We collected 12-lead ECG data from 2541 patients with light chain or transthyretin CA seen at Mayo Clinic between 2000 and 2019. Cases were nearest neighbor matched for age and sex, with 2454 controls. A subset of 2997 (60%) cases and controls were used to train a deep neural network to predict the presence of CA with an internal validation set (n=999; 20%) and a randomly selected holdout testing set (n=999; 20%). We performed experiments using single-lead and 6-lead ECG subsets. RESULTS: The area under the receiver operating characteristic curve (AUC) was 0.91 (CI, 0.90 to 0.93), with a positive predictive value for detecting either type of CA of 0.86. By use of a cutoff probability of 0.485 determined by the Youden index, 426 (84%) of the holdout patients with CA were detected by the model. Of the patients with CA and prediagnosis electrocardiographic studies, the AI model successfully predicted the presence of CA more than 6 months before the clinical diagnosis in 59%. The best single-lead model was V5 with an AUC of 0.86 and a precision of 0.78, with other single leads performing similarly. The 6-lead (bipolar leads) model had an AUC of 0.90 and a precision of 0.85. CONCLUSION: An AI-driven ECG model effectively detects CA and may promote early diagnosis of this life-threatening disease.

Electrocardiogram screening for aortic valve stenosis using artificial intelligence.

AIMS: Early detection of aortic stenosis (AS) is becoming increasingly important with a better outcome after aortic valve replacement in asymptomatic severe AS patients and a poor outcome in moderate AS. We aimed to develop artificial intelligence-enabled electrocardiogram (AI-ECG) using a convolutional neural network to identify patients with moderate to severe AS. METHODS AND RESULTS: Between 1989 and 2019, 258 607 adults [mean age 63 ± 16.3 years; women 122 790 (48%)] with an echocardiography and an ECG performed within 180 days were identified from the Mayo Clinic database. Moderate to severe AS by echocardiography was present in 9723 (3.7%) patients. Artificial intelligence training was performed in 129 788 (50%), validation in 25 893 (10%), and testing in 102 926 (40%) randomly selected subjects. In the test group, the AI-ECG labelled 3833 (3.7%) patients as positive with the area under the curve (AUC) of 0.85. The sensitivity, specificity, and accuracy were 78%, 74%, and 74%, respectively. The sensitivity increased and the specificity decreased as age increased. Women had lower sensitivity but higher specificity compared with men at any age groups. The model performance increased when age and sex were added to the model (AUC 0.87), which further increased to 0.90 in patients without hypertension. Patients with false-positive AI-ECGs had twice the risk for developing moderate or severe AS in 15 years compared with true negative AI-ECGs (hazard ratio 2.18, 95% confidence interval 1.90-2.50). CONCLUSION: An AI-ECG can identify patients with moderate or severe AS and may serve as a powerful screening tool for AS in the community.

Right Ventricular Pulmonary Artery Coupling and Mortality in Cardiac Intensive Care Unit Patients.

Background Impaired right ventricular (RV) pulmonary artery coupling has been associated with higher mortality in patients with chronic heart disease, but few studies have examined this metric in critically ill patients. We sought to evaluate the association between RV pulmonary artery coupling, defined by the ratio of tricuspid annular peak systolic tissue Doppler velocity (TASV)/estimated RV systolic pressure (RVSP), and mortality in cardiac intensive care unit patients. Methods and Results Using a database of unique cardiac intensive care unit admissions from 2007 to 2018, we included patients with TASV/RVSP ratio measured within 1 day of hospitalization. Hospital mortality was analyzed using multivariable logistic regression, and 1-year mortality was analyzed using multivariable Cox proportional-hazards analysis. We included 4259 patients with a mean age of 69±15 years (40.1% women). Admission diagnoses included acute coronary syndrome in 56%, heart failure in 52%, respiratory failure in 24%, and cardiogenic shock in 12%. The mean TASV/RVSP ratio was 0.31±0.14, and in-hospital mortality occurred in 7% of patients. Higher TASV/RVSP ratio was associated with lower in-hospital mortality (adjusted unit odds ratio, 0.68 per each 0.1-unit higher ratio; 95% CI, 0.58-0.79; P<0.001) and lower 1-year mortality among hospital survivors (adjusted unit hazard ratio, 0.83 per each 0.1-unit higher ratio; 95% CI, 0.77-0.90; P<0.001). Stepwise decreases in hospital and 1-year mortality were observed in each higher TASV/RVSP quintile. The TASV/RVSP ratio remained associated with mortality after adjusting for left ventricular systolic and diastolic function. Conclusions A low TASV/RVSP ratio is associated with increased short-term and long-term mortality among cardiac intensive care unit patients, emphasizing importance of impaired RV pulmonary artery coupling as a determinant of poor prognosis. Further study is required to determine whether interventions to optimize RV pulmonary artery coupling can improve outcomes.

Improving the delivery of palliative care through predictive modeling and healthcare informatics.

OBJECTIVE: Access to palliative care (PC) is important for many patients with uncontrolled symptom burden from serious or complex illness. However, many patients who could benefit from PC do not receive it early enough or at all. We sought to address this problem by building a predictive model into a comprehensive clinical framework with the aims to (i) identify in-hospital patients likely to benefit from a PC consult, and (ii) intervene on such patients by contacting their care team. MATERIALS AND METHODS: Electronic health record data for 68 349 inpatient encounters in 2017 at a large hospital were used to train a model to predict the need for PC consult. This model was published as a web service, connected to institutional data pipelines, and consumed by a downstream display application monitored by the PC team. For those patients that the PC team deems appropriate, a team member then contacts the patient's corresponding care team. RESULTS: Training performance AUC based on a 20% holdout validation set was 0.90. The most influential variables were previous palliative care, hospital unit, Albumin, Troponin, and metastatic cancer. The model has been successfully integrated into the clinical workflow making real-time predictions on hundreds of patients per day. The model had an "in-production" AUC of 0.91. A clinical trial is currently underway to assess the effect on clinical outcomes. CONCLUSIONS: A machine learning model can effectively predict the need for an inpatient PC consult and has been successfully integrated into practice to refer new patients to PC.

Machine learning aids clinical decision-making in patients presenting with angina and non-obstructive coronary artery disease.

Aims: The current gold standard comprehensive assessment of coronary microvascular dysfunction (CMD) is through a limited-access invasive catheterization lab procedure. We aimed to develop a point-of-care tool to assist clinical guidance in patients presenting with chest pain and/or an abnormal cardiac functional stress test and with non-obstructive coronary artery disease (NOCAD). Methods and results: This study included 1893 NOCAD patients (<50% angiographic stenosis) who underwent CMD evaluation as well as an electrocardiogram (ECG) up to 1-year prior. Endothelial-independent CMD was defined by coronary flow reserve (CFR) ≤2.5 in response to intracoronary adenosine. Endothelial-dependent CMD was defined by a maximal percent increase in coronary blood flow (%ΔCBF) ≤50% in response to intracoronary acetylcholine infusion. We trained algorithms to distinguish between the following outcomes: CFR ≤2.5, %ΔCBF ≤50, and the combination of both. Two classes of algorithms were trained, one depending on ECG waveforms as input, and another using tabular clinical data. Mean age was 51 ± 12 years and 66% were females (n = 1257). Area under the curve values ranged from 0.49 to 0.67 for all the outcomes. The best performance in our analysis was for the outcome CFR ≤2.5 with clinical variables. Area under the curve and accuracy were 0.67% and 60%. When decreasing the threshold of positivity, sensitivity and negative predictive value increased to 92% and 90%, respectively, while specificity and positive predictive value decreased to 25% and 29%, respectively. Conclusion: An artificial intelligence-enabled algorithm may be able to assist clinical guidance by ruling out CMD in patients presenting with chest pain and/or an abnormal functional stress test. This algorithm needs to be prospectively validated in different cohorts.

Identification of stage I/IIA melanoma patients at high risk for disease relapse using a clinicopathologic and gene expression model.

PURPOSE: Patients with stage I/IIA cutaneous melanoma (CM) are currently not eligible for adjuvant therapies despite uncertainty in relapse risk. Here, we studied the ability of a recently developed model which combines clinicopathologic and gene expression variables (CP-GEP) to identify stage I/IIA melanoma patients who have a high risk for disease relapse. PATIENTS AND METHODS: Archival specimens from a cohort of 837 consecutive primary CMs were used for assessing the prognostic performance of CP-GEP. The CP-GEP model combines Breslow thickness and patient age, with the expression of eight genes in the primary tumour. Our specific patient group, represented by 580 stage I/IIA patients, was stratified based on their risk of relapse: CP-GEP High Risk and CP-GEP Low Risk. The main clinical end-point of this study was five-year relapse-free survival (RFS). RESULTS: Within the stage I/IIA melanoma group, CP-GEP identified a high-risk patient group (47% of total stage I/IIA patients) which had a considerably worse five-year RFS than the low-risk patient group; 74% (95% confidence interval [CI]: 67%-80%) versus 89% (95% CI: 84%-93%); hazard ratio [HR] = 2.98 (95% CI: 1.78-4.98); P < 0.0001. Of patients in the high-risk group, those who relapsed were most likely to do so within the first 3 years. CONCLUSION: The CP-GEP model can be used to identify stage I/IIA patients who have a high risk for disease relapse. These patients may benefit from adjuvant therapy.

ß3 integrin immunohistochemistry as a method to predict sentinel lymph node status in patients with primary cutaneous melanoma.

BACKGROUND: Integrins are heterodimeric proteins composed of noncovalently linked ɑ and ß subunits which are essential for a wide range of normal physiology and also play prominent roles in cancer. Here we tested whether integrin expression in diagnostic skin biopsies is associated with sentinel lymph node (SLN) metastasis. METHODS: We utilized a cohort of 854 consecutive patients with primary cutaneous melanoma to quantify the expression of ß integrin subunits by reverse transcriptase quantitative PCR (RT-qPCR). In addition, we quantified the expression of ß3 integrin by immunohistochemistry (IHC) in a subset of 271 patients by H score. Outcome of interest was SLN biopsy metastasis within 90 days of melanoma diagnosis. Logistic regression analyses were used to develop models for the likelihood of SLN metastasis from molecular, clinical, and histologic variables. RESULTS: ß3 integrin expression quantified by IHC or RT-qPCR was associated with SLN metastasis. ß1, ß5, ß6, and ß8 integrin expression was not associated with SLN metastasis. The incremental gain in performance of a predictive model which included ß3 integrin expression as quantified by IHC in combination with established clinicopathologic variables (Breslow depth and patient age) was limited. CONCLUSIONS: ß3 integrin is the principal integrin subunit associated with sentinel lymph node biopsy (SLNb) metastasis in primary cutaneous melanoma. However, ß3 integrin H score does not significantly improve models for the likelihood of SLN metastasis over Breslow depth and patient age.

Utilization of Palliative Care for Cardiogenic Shock Complicating Acute Myocardial Infarction: A 15-Year National Perspective on Trends, Disparities, Predictors, and Outcomes.

Background This study sought to evaluate the 15-year national utilization, trends, predictors, disparities, and outcomes of palliative care services (PCS) use in cardiogenic shock complicating acute myocardial infarction. Methods and Results A retrospective cohort from January 1, 2000 through December 31, 2014 was analyzed using the National Inpatient Sample database. Administrative codes for acute myocardial infarction-cardiogenic shock and PCS were used to identify eligible admissions. The primary outcomes were the frequency, utilization trends, and predictors of PCS. Secondary outcomes included in-hospital mortality and resources utilization. Multivariable regression and propensity-matching analyses were used to control for confounding. In this 15-year period, there were 444 253 acute myocardial infarction-cardiogenic shock admissions, of which 4.5% received PCS. The cohort receiving PCS was older, of white race, female sex, and with higher comorbidity and acute organ failure. The PCS cohort received fewer cardiac procedures, but more noncardiac organ support therapies. Older age, female sex, white race, higher comorbidity, higher socioeconomic status, admission to a larger hospital, and admission after 2008 were independent predictors of PCS use. Use of PCS was independently associated with higher in-hospital mortality (odds ratio 6.59 [95% CI 6.37-6.83]; P<0.001). The cohort with PCS use had >2-fold higher in-hospital mortality, 12-fold higher use of do-not-resuscitate status, lesser in-hospital resource utilization, and fewer discharges to home. Similar findings were observed in the propensity-matched cohort. Conclusions PCS use in patients with acute myocardial infarction-cardiogenic shock is low, though there is a trend towards increased adoption. There are significant patient and hospital-specific disparities in the utilization of PCS.

Measuring the impact of ambulatory red blood cell transfusion on home functional status: study protocol for a pilot randomized controlled trial.

BACKGROUND: Red blood cell (RBC) transfusion is frequently employed in both ambulatory and hospital environments with the aim of improving patient functional status. In the ambulatory setting, this practice is particularly common in patients with malignancy due to anemia associated with their cancer therapy. Increasingly, the efficacy of this US$10.5 billion per year practice has been called into question. While it is often standard of care for patients with chemotherapy-induced anemia to receive ambulatory RBC transfusions, it is unclear to what extent such transfusions affect home functional status. It is also unclear whether or not changes in functional status in this population can be objectively quantified using wearable activity monitors. We propose to directly measure the impact of outpatient RBC transfusions on at-home functional status by recording several physiological parameters and quantifiable physical activity metrics, e.g., daily energy expenditure and daily total step count, using the ActiGraph wGT3X-BT. This device is an accelerometer-based wearable activity monitor similar in size to a small watch and is worn at the waist. Study participants will wear the device during the course of their daily activities giving us quantifiable insight into activity levels in the home environment. METHODS/DESIGN: This will be a randomized crossover pilot clinical trial with a participant study duration of 28 days. The crossover nature allows each patient to serve as their own control. Briefly, patients presenting at a tertiary medical center's Ambulatory Infusion Center (AIC) will be randomized to either: (1) receive an RBC transfusion as scheduled (transfusion) or (2) abstain from the scheduled transfusion (no transfusion). After an appropriate washout period, participants will crossover from the transfusion arm to the no-transfusion arm or vice versa. Activity levels will be recorded continuously throughout the study using an accelerometry monitor. In addition to device data, functional status and health outcomes will be collected via a weekly telephone interview. The primary outcome measure will be daily energy expenditure. Performance metrics, such as step count changes, will also be evaluated. Additional secondary outcome measures will include daily sedentary time and Patient-reported Outcomes Measurement Information System (PROMIS) Global 10 Survey scores. DISCUSSION: This trial will provide important information on the feasibility and utility of using accelerometry monitors to directly assess the impact of RBC transfusion on patients' functional status. The results of the study will inform the merit and methods of a more definitive future trial evaluating the impact of ambulatory RBC transfusions in the target population. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02835937 . Registered on 15 July 2016.

Automated Diabetes Case Identification Using Electronic Health Record Data at a Tertiary Care Facility.

OBJECTIVE: To develop and validate a phenotyping algorithm for the identification of patients with type 1 and type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic health records. PATIENTS AND METHODS: We used first-order logic rules (if-then-else rules) to imply the presence or absence of DM types 1 and 2. The "if" clause of each rule is a conjunction of logical and, or predicates that provides evidence toward or against the presence of DM. The rule includes International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes, outpatient prescription information, laboratory values, and positive annotation of DM in patients' clinical notes. This study was conducted from March 2, 2015, through February 10, 2016. The performance of our rule-based approach and similar approaches proposed by other institutions was evaluated with a reference standard created by an expert reviewer and implemented for routine clinical care at an academic medical center. RESULTS: A total of 4208 surgical patients (mean age, 52 years; males, 48%) were analyzed to develop the phenotyping algorithm. Expert review identified 685 patients (16.28% of the full cohort) as having DM. Our proposed method identified 684 patients (16.25%) as having DM. The algorithm performed well-99.70% sensitivity, 99.97% specificity-and compared favorably with previous approaches. CONCLUSION: Among patients undergoing surgery, determination of DM can be made with high accuracy using simple, computationally efficient rules. Knowledge of patients' DM status before surgery may alter physicians' care plan and reduce postsurgical complications. Nevertheless, future efforts are necessary to determine the effect of first-order logic rules on clinical processes and patient outcomes.

Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of these methods apply essentially to an "average" patient. However, as patients show significant heterogeneity in their responses to treatments, this average effect is of limited value. It does not account for individual level variabilities, which can deviate substantially from the population average. To address this critical problem, we present a framework that allows the discovery of clinically meaningful homogeneous subgroups with differential effects of plasma transfusion using unsupervised random forest clustering. Subgroup analysis using two blood transfusion datasets show that considerable variablilities exist between the subgroups and population in both the treatment effect of plasma transfusion on bleeding and mortality and risk factors for these outcomes. These results support the customization of blood transfusion therapy for the individual patient.