Clinical Phenotyping with an Outcomes-driven Mixture of Experts for Patient Matching and Risk Estimation.

Observational medical data present unique opportunities for analysis of medical outcomes and treatment decision making. However, because these datasets do not contain the strict pairing of randomized control trials, matching techniques are to draw comparisons among patients. A key limitation to such techniques is verification that the variables used to model treatment decision making are also relevant in identifying the risk of major adverse events. This article explores a deep mixture of experts approach to jointly learn how to match patients and model the risk of major adverse events in patients. Although trained with information regarding treatment and outcomes, after training, the proposed model is decomposable into a network that clusters patients into phenotypes from information available before treatment. This model is validated on a dataset of patients with acute myocardial infarction complicated by cardiogenic shock. The mixture of experts approach can predict the outcome of mortality with an area under the receiver operating characteristic curve of 0.85 ± 0.01 while jointly discovering five potential phenotypes of interest. The technique and interpretation allow for identifying clinically relevant phenotypes that may be used both for outcomes modeling as well as potentially evaluating individualized treatment effects.

A prediction model for asthma exacerbations after stopping asthma biologics.

BACKGROUND: Little is known regarding the prediction of the risks of asthma exacerbation after stopping asthma biologics. OBJECTIVE: To develop and validate a predictive model for the risk of asthma exacerbations after stopping asthma biologics using machine learning models. METHODS: We identified 3057 people with asthma who stopped asthma biologics in the OptumLabs Database Warehouse and considered a wide range of demographic and clinical risk factors to predict subsequent outcomes. The primary outcome used to assess success after stopping was having no exacerbations in the 6 months after stopping the biologic. Elastic-net logistic regression (GLMnet), random forest, and gradient boosting machine models were used with 10-fold cross-validation within a development (80%) cohort and validation cohort (20%). RESULTS: The mean age of the total cohort was 47.1 (SD, 17.1) years, 1859 (60.8%) were women, 2261 (74.0%) were White, and 1475 (48.3%) were in the Southern region of the United States. The elastic-net logistic regression model yielded an area under the curve (AUC) of 0.75 (95% confidence interval [CI], 0.71-0.78) in the development and an AUC of 0.72 in the validation cohort. The random forest model yielded an AUC of 0.75 (95% CI, 0.68-0.79) in the development cohort and an AUC of 0.72 in the validation cohort. The gradient boosting machine model yielded an AUC of 0.76 (95% CI, 0.72-0.80) in the development cohort and an AUC of 0.74 in the validation cohort. CONCLUSION: Outcomes after stopping asthma biologics can be predicted with moderate accuracy using machine learning methods.

Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrosing interstitial lung disease with a mean survival time of less than 5 years. Nonspecific presentation, a lack of effective early screening tools, unclear pathobiology of early-stage IPF and the need for invasive and expensive procedures for diagnostic confirmation hinder early diagnosis. In this study, we introduce a new screening tool for IPF in primary care settings that requires no new laboratory tests and does not require recognition of early symptoms. Using subtle comorbidity signatures identified from the history of medical encounters of individuals, we developed an algorithm, called the zero-burden comorbidity risk score for IPF (ZCoR-IPF), to predict the future risk of an IPF diagnosis. ZCoR-IPF was trained on a national insurance claims database and validated on three independent databases, comprising a total of 2,983,215 participants, with 54,247 positive cases. The algorithm achieved positive likelihood ratios greater than 30 at a specificity of 0.99 across different cohorts, for both sexes, and for participants with different risk states and history of confounding diseases. The area under the receiver-operating characteristic curve for ZCoR-IPF in predicting IPF exceeded 0.88 and was approximately 0.84 at 1 and 4 years before a conventional diagnosis, respectively. Thus, if adopted, ZCoR-IPF can potentially enable earlier diagnosis of IPF and improve outcomes of disease-modifying therapies and other interventions.

Impact of labor characteristics on maternal and neonatal outcomes of labor: A machine-learning model.

INTRODUCTION: Since Friedman's seminal publication on laboring women, numerous publications have sought to define normal labor progress. However, there is paucity of data on contemporary labor cervicometry incorporating both maternal and neonatal outcomes. The objective of this study is to establish intrapartum prediction models of unfavorable labor outcomes using machine-learning algorithms. MATERIALS AND METHODS: Consortium on Safe Labor is a large database consisting of pregnancy and labor characteristics from 12 medical centers in the United States. Outcomes, including maternal and neonatal outcomes, were retrospectively collected. We defined primary outcome as the composite of following unfavorable outcomes: cesarean delivery in active labor, postpartum hemorrhage, intra-amniotic infection, shoulder dystocia, neonatal morbidity, and mortality. Clinical and obstetric parameters at admission and during labor progression were used to build machine-learning risk-prediction models based on the gradient boosting algorithm. RESULTS: Of 228,438 delivery episodes, 66,586 were eligible for this study. Mean maternal age was 26.95 ± 6.48 years, mean parity was 0.92 ± 1.23, and mean gestational age was 39.35 ± 1.13 weeks. Unfavorable labor outcome was reported in 14,439 (21.68%) deliveries. Starting at a cervical dilation of 4 cm, the area under receiver operating characteristics curve (AUC) of prediction models increased from 0.75 (95% confidence interval, 0.75-0.75) to 0.89 (95% confidence interval, 0.89-0.90) at a dilation of 10 cm. Baseline labor risk score was above 35% in patients with unfavorable outcomes compared to women with favorable outcomes, whose score was below 25%. CONCLUSION: Labor risk score is a machine-learning-based score that provides individualized and dynamic alternatives to conventional labor charts. It predicts composite of adverse birth, maternal, and neonatal outcomes as labor progresses. Therefore, it can be deployed in clinical practice to monitor labor progress in real time and support clinical decisions.

Identifying Patients With Advanced Heart Failure Using Administrative Data.

Objective: To develop algorithms to identify patients with advanced heart failure (HF) that can be applied to administrative data. Patients and Methods: In a population-based cohort of all residents of Olmsted County, Minnesota, with greater than or equal to 1 HF billing code 2007-2017 (n=8657), we identified all patients with advanced HF (n=847) by applying the gold standard European Society of Cardiology advanced HF criteria via manual medical review by an HF cardiologist. The advanced HF index date was the date the patient first met all European Society of Cardiology criteria. We subsequently developed candidate algorithms to identify advanced HF using administrative data (billing codes and prescriptions relevant to HF or comorbidities that affect HF outcomes), applied them to the HF cohort, and assessed their ability to identify patients with advanced HF on or after their advanced HF index date. Results: A single hospitalization for HF or ventricular arrhythmias identified all patients with advanced HF (sensitivity, 100%); however, the positive predictive value (PPV) was low (36.4%). More stringent definitions, including additional hospitalizations and/or other signs of advanced HF (hyponatremia, acute kidney injury, hypotension, or high-dose diuretic use), decreased the sensitivity but improved the specificity and PPV. For example, 2 hospitalizations plus 1 sign of advanced HF had a sensitivity of 72.7%, specificity of 89.8%, and PPV of 60.5%. Negative predictive values were high for all algorithms evaluated. Conclusion: Algorithms using administrative data can identify patients with advanced HF with reasonable performance.

Introduction to Machine Learning in Obstetrics and Gynecology.

In the digital age of the 21st century, we have witnessed an explosion in data matched by remarkable progress in the field of computer science and engineering, with the development of powerful and portable artificial intelligence-powered technologies. At the same time, global connectivity powered by mobile technology has led to an increasing number of connected users and connected devices. In just the past 5 years, the convergence of these technologies in obstetrics and gynecology has resulted in the development of innovative artificial intelligence-powered digital health devices that allow easy and accurate patient risk stratification for an array of conditions spanning early pregnancy, labor and delivery, and care of the newborn. Yet, breakthroughs in artificial intelligence and other new and emerging technologies currently have a slow adoption rate in medicine, despite the availability of large data sets that include individual electronic health records spanning years of care, genomics, and the microbiome. As a result, patient interactions with health care remain burdened by antiquated processes that are inefficient and inconvenient. A few health care institutions have recognized these gaps and, with an influx of venture capital investments, are now making in-roads in medical practice with digital products driven by artificial intelligence algorithms. In this article, we trace the history, applications, and ethical challenges of the artificial intelligence that will be at the forefront of digitally transforming obstetrics and gynecology and medical practice in general.

Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors.

Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants' free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants' estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users' field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.

Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population.

PURPOSE: The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data. PARTICIPANTS: All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013. FINDINGS TO DATE: For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline. FUTURE PLANS: Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.

Use of Mechanical Circulatory Support Devices Among Patients With Acute Myocardial Infarction Complicated by Cardiogenic Shock.

Importance: Mechanical circulatory support (MCS) devices, including intravascular microaxial left ventricular assist devices (LVADs) and intra-aortic balloon pumps (IABPs), are used in patients who undergo percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) complicated by cardiogenic shock despite limited evidence of their clinical benefit. Objective: To examine trends in the use of MCS devices among patients who underwent PCI for AMI with cardiogenic shock, hospital-level use variation, and factors associated with use. Design, Setting, and Participants: This cross-sectional study used the CathPCI and Chest Pain-MI Registries of the American College of Cardiology National Cardiovascular Data Registry. Patients who underwent PCI for AMI complicated by cardiogenic shock between October 1, 2015, and December 31, 2017, were identified from both registries. Data were analyzed from October 2018 to August 2020. Exposures: Therapies to provide hemodynamic support were categorized as intravascular microaxial LVAD, IABP, TandemHeart, extracorporeal membrane oxygenation, LVAD, other devices, combined IABP and intravascular microaxial LVAD, combined IABP and other device (defined as TandemHeart, extracorporeal membrane oxygenation, LVAD, or another MCS device), or medical therapy only. Main Outcomes and Measures: Use of MCS devices overall and specific MCS devices, including intravascular microaxial LVAD, at both patient and hospital levels and variables associated with use. Results: Among the 28 304 patients included in the study, the mean (SD) age was 65.4 (12.6) years and 18 968 were men (67.0%). The overall MCS device use was constant from the fourth quarter of 2015 to the fourth quarter of 2017, although use of intravascular microaxial LVADs significantly increased (from 4.1% to 9.8%; P < .001), whereas use of IABPs significantly decreased (from 34.8% to 30.0%; P < .001). A significant hospital-level variation in MCS device use was found. The median (interquartile range [IQR]) proportion of patients who received MCS devices was 42% (30%-54%), and the median proportion of patients who received intravascular microaxial LVADs was 1% (0%-10%). In multivariable analyses, cardiac arrest at first medical contact or during hospitalization (odds ratio [OR], 1.82; 95% CI, 1.58-2.09) and severe left main and/or proximal left anterior descending coronary artery stenosis (OR, 1.36; 95% CI, 1.20-1.54) were patient characteristics that were associated with higher odds of receiving intravascular microaxial LVADs only compared with IABPs only. Conclusions and Relevance: This study found that, among patients who underwent PCI for AMI complicated by cardiogenic shock, overall use of MCS devices was constant, and a 2.5-fold increase in intravascular microaxial LVAD use was found along with a corresponding decrease in IABP use and a significant hospital-level variation in MCS device use. These trends were observed despite limited clinical trial evidence of improved outcomes associated with device use.

Comparative Effectiveness and Safety of Oral Anticoagulants Across Kidney Function in Patients With Atrial Fibrillation.

BACKGROUND: Patients with atrial fibrillation and severely decreased kidney function were excluded from the pivotal non-vitamin K antagonist oral anticoagulants (NOAC) trials, thereby raising questions about comparative safety and effectiveness in patients with reduced kidney function. The study aimed to compare oral anticoagulants across the range of kidney function in patients with atrial fibrillation. METHODS AND RESULTS: Using a US administrative claims database with linked laboratory data, 34 569 new users of oral anticoagulants with atrial fibrillation and estimated glomerular filtration rate ≥15 mL/(min·1.73 m2) were identified between October 1, 2010 to November 29, 2017. The proportion of patients using NOACs declined with decreasing kidney function-73.5%, 69.6%, 65.4%, 59.5%, and 45.0% of the patients were prescribed a NOAC in estimated glomerular filtration rate ≥90, 60 to 90, 45 to 60, 30 to 45, 15 to 30 mL/min per 1.73 m2 groups, respectively. Stabilized inverse probability of treatment weighting was used to balance 4 treatment groups (apixaban, dabigatran, rivaroxaban, and warfarin) on 66 baseline characteristics. In comparison to warfarin, apixaban was associated with a lower risk of stroke (hazard ratio [HR], 0.57 [0.43-0.75]; P<0.001), major bleeding (HR, 0.51 [0.44-0.61]; P<0.001), and mortality (HR, 0.68 [0.56-0.83]; P<0.001); dabigatran was associated with a similar risk of stroke but a lower risk of major bleeding (HR, 0.57 [0.43-0.75]; P<0.001) and mortality (HR, 0.68 [0.48-0.98]; P=0.04); rivaroxaban was associated with a lower risk of stroke (HR, 0.69 [0.51-0.94]; P=0.02), major bleeding (HR, 0.84 [0.72-0.99]; P=0.04), and mortality (HR, 0.73 [0.58-0.91]; P=0.006). There was no significant interaction between treatment and estimated glomerular filtration rate categories for any outcome. When comparing one NOAC to another NOAC, there was no significant difference in mortality, but some differences existed for stroke or major bleeding. No relationship between treatments and falsification end points was found, suggesting no evidence for substantial residual confounding. CONCLUSIONS: Relative to warfarin, NOACs are used less frequently as kidney function declines. However, NOACs appears to have similar or better comparative effectiveness and safety across the range of kidney function.

Quality over quantity: skeletal loading intensity plays a key role in understanding the relationship between physical activity and bone density in postmenopausal women.

OBJECTIVE: Increasing physical activity (PA) is regularly cited as a modifiable target to improve health outcomes and quality of life in the aging population, especially postmenopausal women who exhibit low bone mineral density (BMD) and high fracture risk. In this cross-sectional study, we aimed to quantify real-world PA and its association with BMD in postmenopausal women. METHODS: Seventy postmenopausal women, aged 46 to 79 years, received a dual-energy X-ray absorptiometry scan measuring total hip BMD and wore bilateral triaxial accelerometers on the ankles for 7 days to measure PA in their free-living environment. Custom step detection and peak vertical ground reaction force estimation algorithms, sensitive to both quantity and intensity of PA, were used to calculate a daily bone density index (BDI) for each participant. Multiple regression was used to quantify the relationship between total hip BMD, age, step counts, and mean BDI over the span of 7 days of data collection. RESULTS: All participants completed the full 7 days of PA monitoring, totaling more than 7 million detected steps. Participants averaged 14,485 ±â€Š4,334 steps daily with mean peak vertical ground reaction force stepping loads of 675 ±â€Š121 N during daily living. Across the population, total hip BMD was found to be significantly correlated with objective estimates of mean BDI (r = 0.44), as well as participant age (r = 0.285). CONCLUSION: Despite having higher-than-expected PA, the low stepping loads observed in this cohort, along with half of the participants having low BMD measures, underscores the need for PA intensity to be considered in the management of postmenopausal bone health.

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia.

PURPOSE: Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly heterogeneous presentations and prognoses, as in chronic lymphocytic lymphoma (CLL). Although machine learning methods can readily learn complex nonlinear relationships, many methods are criticized as inadequate because of limited interpretability. We propose using unsupervised clustering of the continuous output of machine learning models to provide discrete risk stratification for predicting time to first treatment in a cohort of patients with CLL. PATIENTS AND METHODS: A total of 737 treatment-naïve patients with CLL diagnosed at Mayo Clinic were included in this study. We compared predictive abilities for two survival models (Cox proportional hazards and random survival forest) and four classification methods (logistic regression, support vector machines, random forest, and gradient boosting machine). Probability of treatment was then stratified. RESULTS: Machine learning methods did not yield significantly more accurate predictions of time to first treatment. However, automated risk stratification provided by clustering was able to better differentiate patients who were at risk for treatment within 1 year than models developed using standard survival analysis techniques. CONCLUSION: Clustering the posterior probabilities of machine learning models provides a way to better interpret machine learning models.

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.