Patient risk stratification with time-varying parameters: A multitask learning approach

The proliferation of electronic health records (EHRs) frames opportunities for using machine learning to build models that help healthcare providers improve patient outcomes. However, building useful risk stratification models presents many technical challenges including the large number of factors (both intrinsic and extrinsic) influencing a patient's risk of an adverse outcome and the inherent evolution of that risk over time. We address these challenges in the context of learning a risk stratification model for predicting which patients are at risk of acquiring a Clostridium difficile infection (CDI). We take a novel data-centric approach, leveraging the contents of EHRs from nearly 50,000 hospital admissions. We show how, by adapting techniques from multitask learning, we can learn models for patient risk stratification with unprecedented classification performance. Our model, based on thousands of variables, both time-varying and time-invariant, changes over the course of a patient admission. Applied to a held out set of approximately 25,000 patient admissions, we achieve an area under the receiver operating characteristic curve of 0.81 (95% CI 0.78-0.84). The model has been integrated into the health record system at a large hospital in the US, and can be used to produce daily risk estimates for each inpatient. While more complex than traditional risk stratification methods, the widespread development and use of such data-driven models could ultimately enable cost-effective, targeted prevention strategies that lead to better patient outcomes

Learning Credible Models

In many settings, it is important that a model be capable of providing reasons for its predictions (i.e., the model must be interpretable). However, the model's reasoning may not conform with well-established knowledge. In such cases, while interpretable, the model lacks \textit{credibility}. In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible. In particular, we propose a regularization penalty, expert yielded estimates (EYE), that incorporates expert knowledge about well-known relationships among covariates and the outcome of interest. We give both theoretical and empirical results comparing our proposed method to several other regularization techniques. Across a range of settings, experiments on both synthetic and real data show that models learned using the EYE penalty are significantly more credible than those learned using other penalties. Applied to a large-scale patient risk stratification task, our proposed technique results in a model whose top features overlap significantly with known clinical risk factors, while still achieving good predictive performance

Development Of The Acute Decompensated Heart Failure Risk Model For Emergency Room Resident Training

The purpose of this study was to characterize patients hospitalized with acute decompensated heart failure with and without low systolic blood pressure using exploratory factor analysis (EFA). Direct and surrogate measurements were measured. The aim was to use EFA for data reduction to elicit a parsimonious set of factors summarizing the relationships between variables by measuring intercorrelations of the clinical variables collected as part of standard care, and abstracted from electronic medical records. A better understanding of the characteristics and outcomes of the target group could potentially lead to individualized treatment modalities tailored to effectively and economically improve care. Patients hospitalized are at a high risk for adverse outcomes after discharge. Prospectively collected new data is expensive, labor-, and time- intensive while the use of existing data allows a quicker, more efficient and less expensive source. A large urban, academic teaching hospital was the study site. Wayne State University Human Investigation Committee and Henry Ford Internal Review expedited review approval was obtained. Eligible cases were patients hospitalized with a primary diagnosis of acute decompensated heart failure for the 2014 year. Variables collected were identified based on review of the literature, Framingham criteria, clinical relevance, and were routinely availability. As is the case in empirical studies, determining sample size in EFA, a large sample size technique, is based on the minimum necessary to obtain reliable results from the analysis. Guidelines or a rule of thumb by expert opinions such as Gorsuch (1983) and Kline (1994) include absolute numbers of at least 100 cases. Dimension reduction of factors via SPSS (ver 23) was conducted on all cases regardless of presenting systolic blood pressure (Group 1), cases with normal to high systolic blood pressure (Group 2) and cases with low systolic blood pressure (Group 3) separately, for a total of groups. All cases were screened for entry criteria and the first 300 chronologically dated cases were identified. EFA was conducted on the data abstracted from 300 electronic medical records. The major findings of the study were that two factors, Anemia and Kidney Function were seen across the three groups. Several individual factors that affect kidney function were found. Data reduction using EFA is a highly pragmatic function. Computer software programs such as SPSS® allow for quick and easy computations and a large number of variables can be directly imported from databases such as Excel®. However, EFA is a complex procedure with fewer absolute guidelines or rules for selecting options compared to other statistical approaches. The steps taken were detailed, justified by the literature reviewed and alternate choices were discussed. The seven stages in factor analysis design as outlined by Hair et al. (2006) were employed in this analysis. The factors identified in each group using EFA can be tested in a future confirmatory factor analysis study. Once these factors are the confirmed, an Acute Decompensated Heart Failure Risk Model can be developed for Emergency Room Resident Training within the context of evidence-based medicine. The pedagogical approach in medical education where instruction is provided by the experienced physician to the novice, namely the medical resident, is in conflict of adult learning theory leading to a contributing factor to the success or failure of teaching evidence-based medicine. Risk models are powerful tools for assessing biomedical significance but the importance of how to teach and use a risk model cannot be underestimated. Building on what emergency room residents may know, or determining whether there is a knowledge deficit is extremely important. A step-by-step process layering information on what is already known (present level of understanding) by the leaner o the required knowledge level is needed. The results of the EFA conducted indicates that patients with and without low systolic blood pressure share common factors. These factors, anemia and kidney function also directly affect blood pressure. If emergency room residents do not know that these factors are shared, then the first step would be to educate them about this finding. If emergency room residents do know from prior knowledge, then the teacher would be adding to their knowledge base when teaching the residents the use of the risk model as is described by Knowles, Holton, and Swanson (2005) as the first underlying assumption. The shift to student centered learning is based on adult learning theory (Spencer, 1999) and transformational learning should be employed

Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial

Background: Intraventricular haemorrhage is a subtype of intracerebral haemorrhage, with 50% mortality and serious disability for survivors. We aimed to test whether attempting to remove intraventricular haemorrhage with alteplase versus saline irrigation improved functional outcome. Methods: In this randomised, double-blinded, placebo-controlled, multiregional trial (CLEAR III), participants with a routinely placed extraventricular drain, in the intensive care unit with stable, non-traumatic intracerebral haemorrhage volume less than 30 mL, intraventricular haemorrhage obstructing the 3rd or 4th ventricles, and no underlying pathology were adaptively randomly assigned (1:1), via a web-based system to receive up to 12 doses, 8 h apart of 1 mg of alteplase or 0·9% saline via the extraventricular drain. The treating physician, clinical research staff, and participants were masked to treatment assignment. CT scans were obtained every 24 h throughout dosing. The primary efficacy outcome was good functional outcome, defined as a modified Rankin Scale score (mRS) of 3 or less at 180 days per central adjudication by blinded evaluators. This study is registered with ClinicalTrials.gov, NCT00784134. Findings: Between Sept 18, 2009, and Jan 13, 2015, 500 patients were randomised: 249 to the alteplase group and 251 to the saline group. 180-day follow-up data were available for analysis from 246 of 249 participants in the alteplase group and 245 of 251 participants in the placebo group. The primary efficacy outcome was similar in each group (good outcome in alteplase group 48% vs saline 45%; risk ratio [RR] 1·06 [95% CI 0·88–1·28; p=0·554]). A difference of 3·5% (RR 1·08 [95% CI 0·90–1·29], p=0·420) was found after adjustment for intraventricular haemorrhage size and thalamic intracerebral haemorrhage. At 180 days, the treatment group had lower case fatality (46 [18%] vs saline 73 [29%], hazard ratio 0·60 [95% CI 0·41–0·86], p=0·006), but a greater proportion with mRS 5 (42 [17%] vs 21 [9%]; RR 1·99 [95% CI 1·22–3·26], p=0·007). Ventriculitis (17 [7%] alteplase vs 31 [12%] saline; RR 0·55 [95% CI 0·31–0·97], p=0·048) and serious adverse events (114 [46%] alteplase vs 151 [60%] saline; RR 0·76 [95% CI 0·64–0·90], p=0·002) were less frequent with alteplase treatment. Symptomatic bleeding (six [2%] in the alteplase group vs five [2%] in the saline group; RR 1·21 [95% CI 0·37–3·91], p=0·771) was similar. Interpretation: In patients with intraventricular haemorrhage and a routine extraventricular drain, irrigation with alteplase did not substantially improve functional outcomes at the mRS 3 cutoff compared with irrigation with saline. Protocol-based use of alteplase with extraventricular drain seems safe. Future investigation is needed to determine whether a greater frequency of complete intraventricular haemorrhage removal via alteplase produces gains in functional status