Identifying Patterns of Medical Intervention in Acute Respiratory Failure: A Retrospective Observational Study.

IMPORTANCE: Characterizing medical interventions delivered to ICU patients over time and their relationship to outcomes can help set expectations and inform decisions made by patients, clinicians, and health systems. OBJECTIVES: To determine whether distinct and clinically relevant pathways of medical intervention can be identified among adult ICU patients with acute respiratory failure. DESIGN SETTING AND PARTICIPANTS: Retrospective observational study using all-payer administrative claims data from 2012 to 2014. Patients were identified from the Healthcare Cost and Utilization Project State Inpatient Databases from Maryland, Massachusetts, Nevada, and Washington. MAIN OUTCOMES AND MEASURES: Patterns of cumulative medical intervention delivery, over time, using temporal k-means clustering of interventions delivered up to hospital days 0, 5, 10, 20, and up to discharge. RESULTS: A total of 12,175 admissions were identified and divided into training (75%; n = 9,130) and validation sets (25%; n = 3,045). Without applying a priori classification and using only medical interventions to cluster, we identified three distinct pathways of intervention accounting for 93.5% of training set admissions. We found 45.9% of admissions followed a "cardiac" intervention pathway (e.g., cardiac catheterization, cardioversion); 36.7% followed a "general" pathway (e.g., diagnostic interventions); and 17.4% followed a "prolonged" pathway (e.g., tracheostomy, gastrostomy). Prolonged pathway admissions had longer median hospital length of stay (13 d; interquartile range [IQR], 7.5-18.5 d) compared with cardiac (5; IQR, 2.5-7.5) and general (5; IQR, 3-7). In-hospital death occurred in 24.6% of prolonged pathway admissions compared with 17.9% of cardiac and 6.9% of general. Findings were confirmed in the validation set. CONCLUSIONS AND RELEVANCE: Most ICU admissions for acute respiratory failure follow one of three clinically relevant pathways of medical intervention which are associated with hospitalization outcomes. This study helps define the longitudinal nature of critical care delivery, which can inform efforts to predict patient outcomes, communicate with patients and their families, and organize critical care resources.

Personalized Breast Cancer Screening.

PURPOSE: Abundant literature and clinical trials indicate that routine cancer screenings decrease patient mortality for several common cancers. However, current national cancer screening guidelines heavily rely on patient age as the predominant factor in deciding cancer screening timing, neglecting other important medical characteristics of individual patients. This approach either delays screening or prescribes excessive screenings. Another disadvantage of the current approach is its inability to combine information across hospital systems because of the lack of a coherent records system. METHODS: We propose to use claims data and medical insurance transactions that use consistent and pre-established sets of codes for diagnosis, procedures, and medications to develop a clinical support tool to supply supplemental insights and precautions for physicians to make more informed decisions. Furthermore, we propose a novel machine learning framework to recommend personalized, data-driven, and dynamic screening decisions. RESULTS: We apply this new method to the study of breast cancer mammograms using claims data from 378,840 female patients to demonstrate that across different risk populations, personalized screening reduces the average delay in a cancer diagnosis by 2-3 months with statistical significance, with even stronger benefits for individual patients up to 10 months. CONCLUSION: Incorporating personal medical characteristics using claims data and novel machine learning methodologies into breast cancer screening improves screening delay by more dynamically considering changing patient risks. Future incorporation of the proposed methodology in health care settings could be provided as a potential support tool for clinicians.

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.

From predictions to prescriptions: A data-driven response to COVID-19.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control's pandemic forecast.

COVID-19 mortality risk assessment: An international multi-center study.

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

Robust radiotherapy planning.

Motion and uncertainty in radiotherapy is traditionally handled via margins. The clinical target volume (CTV) is expanded to a larger planning target volume (PTV), which is irradiated to the prescribed dose. However, the PTV concept has several limitations, especially in proton therapy. Therefore, robust and probabilistic optimization methods have been developed that directly incorporate motion and uncertainty into treatment plan optimization for intensity modulated radiotherapy (IMRT) and intensity modulated proton therapy (IMPT). Thereby, the explicit definition of a PTV becomes obsolete and treatment plan optimization is directly based on the CTV. Initial work focused on random and systematic setup errors in IMRT. Later, inter-fraction prostate motion and intra-fraction lung motion became a research focus. Over the past ten years, IMPT has emerged as a new application for robust planning methods. In proton therapy, range or setup errors may lead to dose degradation and misalignment of dose contributions from different beams - a problem that cannot generally be addressed by margins. Therefore, IMPT has led to the first implementations of robust planning methods in commercial planning systems, making these methods available for clinical use. This paper first summarizes the limitations of the PTV concept. Subsequently, robust optimization methods are introduced and their applications in IMRT and IMPT planning are reviewed.

On correlations in IMRT planning aims.

The purpose was to study correlations amongst IMRT DVH evaluation points and how their relaxation impacts the overall plan. 100 head-and-neck cancer cases, using the Eclipse treatment planning system with the same protocol, are statisti-cally analyzed for PTV, brainstem, and spinal cord. To measure variations amongst the plans, we use (i) interquartile range (IQR) of volume as a function of dose, (ii) interquartile range of dose as a function of volume, and (iii) dose falloff. To determine correlations for institutional and ICRU goals, conditional probabilities and medians are computed. We observe that most plans exceed the median PTV dose (average D50 = 104% prescribed dose). Furthermore, satisfying D50 reduced the probability of also satisfying D98, constituting a negative correlation of these goals. On the other hand, satisfying D50 increased the probability of satisfying D2, suggesting a positive correlation. A positive correlation is also observed between the PTV V105 and V110. Similarly, a positive correlation between the brainstem V45 and V50 is measured by an increase in the conditional median of V45, when V50 is violated. Despite the imposed institutional and international recommenda-tions, significant variations amongst DVH points can occur. Even though DVH aims are evaluated independently, sizable correlations amongst them are possible, indicating that some goals cannot be satisfied concurrently, calling for unbiased plan criteria.

Improving thin-film manufacturing yield with robust optimization.

A novel robust optimization algorithm is demonstrated that is largely deterministic, and yet it attempts to account for statistical variations in coating. Through Monte Carlo simulations of manufacturing, we compare the performance of a proof-of-concept antireflection (AR) coating designed with our robust optimization to that of a conventionally optimized AR coating. We find that the robust algorithm produces an AR coating with a significantly improved yield.

Motion management with phase-adapted 4D-optimization.

Cancer treatment with ionizing radiation is often compromised by organ motion, in particular for lung cases. Motion uncertainties can significantly degrade an otherwise optimized treatment plan. We present a spatiotemporal optimization method, which takes into account all phases of breathing via the corresponding 4D-CTs and provides a 4D-optimal plan that can be delivered throughout all breathing phases. Monte Carlo dose calculations are employed to warrant for highest dosimetric accuracy, as pertinent to study motion effects in lung. We demonstrate the performance of this optimization method with clinical lung cancer cases and compare the outcomes to conventional gating techniques. We report significant improvements in target coverage and in healthy tissue sparing at a comparable computational expense. Furthermore, we show that the phase-adapted 4D-optimized plans are robust against irregular breathing, as opposed to gating. This technique has the potential to yield a higher delivery efficiency and a decisively shorter delivery time.

Dosimetry robustness with stochastic optimization.

All radiation therapy treatment planning relies on accurate dose calculation. Uncertainties in dosimetric prediction can significantly degrade an otherwise optimal plan. In this work, we introduce a robust optimization method which handles dosimetric errors and warrants for high-quality IMRT plans. Unlike other dose error estimations, we do not rely on the detailed knowledge about the sources of the uncertainty and use a generic error model based on random perturbation. This generality is sought in order to cope with a large variety of error sources. We demonstrate the method on a clinical case of lung cancer and show that our method provides plans that are more robust against dosimetric errors and are clinically acceptable. In fact, the robust plan exhibits a two-fold improved equivalent uniform dose compared to the non-robust but optimized plan. The achieved speedup will allow computationally extensive multi-criteria or beam-angle optimization approaches to warrant for dosimetrically relevant plans.

Robust chirped mirrors.

Optimized chirped mirrors may perform suboptimally, or completely fail to satisfy specifications, when manufacturing errors are encountered. We present a robust optimization method for designing these dispersion-compensating mirror systems that are used in ultrashort pulse lasers. Possible implementation errors in layer thickness are taken into account within an uncertainty set. The algorithm identifies worst-case scenarios with respect to reflectivity as well as group delay. An iterative update improves the robustness and warrants a high manufacturing yield, even when the encountered errors are larger than anticipated.

Bose-glass phases in disordered quantum magnets.

In disordered spin systems with antiferromagnetic Heisenberg exchange, transitions into and out of a magnetic-field-induced ordered phase pass through unique regimes. Using quantum Monte Carlo simulations to study the zero-temperature behavior, these intermediate regions are determined to be Bose-glass phases. The localization of field-induced triplons causes a finite compressibility and, hence, glassiness in the disordered phase.