Towards automated calculation of evidence-based clinical scores

Diagnosis of Heart Failure With Preserved Ejection Fraction Among Patients With Unexplained Dyspnea

Importance

Diagnosis of heart failure with preserved ejection fraction (HFpEF) among dyspneic patients without overt congestion is challenging. Multiple diagnostic approaches have been proposed but are not well validated against the independent gold standard for HFpEF diagnosis of an elevated pulmonary capillary wedge pressure (PCWP) during exercise.

Objective

To evaluate H2FPEF and HFA-PEFF scores and a PCWP/cardiac output (CO) slope of more than 2 mm Hg/L/min to diagnose HFpEF.

Design, Setting, and Participants

This retrospective case-control study included patients with unexplained dyspnea from 6 centers in the US, the Netherlands, Denmark, and Australia from March 2016 to October 2020. Diagnosis of HFpEF (cases) was definitively ascertained by the presence of elevated PCWP during exertion; control individuals were those with normal rest and exercise hemodynamics.

Main Outcomes and Measures

Logistic regression was used to evaluate the accuracy of HFA-PEFF and H2FPEF scores to discriminate patients with HFpEF from controls.

Results

Among 736 patients, 563 (76%) were diagnosed with HFpEF (mean [SD] age, 69 [11] years; 334 [59%] female) and 173 (24%) represented controls (mean [SD] age, 60 [15] years; 109 [63%] female). H2FPEF and HFA-PEFF scores discriminated patients with HFpEF from controls, but the H2FPEF score had greater area under the curve (0.845; 95% CI, 0.810-0.875) compared with the HFA-PEFF score (0.710; 95% CI, 0.659-0.756) (difference, -0.134; 95% CI, -0.177 to -0.094; P < .001). Specificity was robust for both scores, but sensitivity was poorer for HFA-PEFF, with a false-negative rate of 55% for low-probability scores compared with 25% using the H2FPEF score. Use of the PCWP/CO slope to redefine HFpEF rather than exercise PCWP reclassified 20% (117 of 583) of patients, but patients reclassified from HFpEF to control by this metric had clinical, echocardiographic, and hemodynamic features typical of HFpEF, including elevated resting PCWP in 66% (46 of 70) of reclassified patients.

Conclusions and Relevance

In this case-control study, despite requiring fewer data, the H2FPEF score had superior diagnostic performance compared with the HFA-PEFF score and PCWP/CO slope in the evaluation of unexplained dyspnea and HFpEF in the outpatient setting.

Development and validation of an interpretable clinical score for early identification of acute kidney injury at the emergency department

Acute kidney injury (AKI) in hospitalised patients is a common syndrome associated with poorer patient outcomes. Clinical risk scores can be used for the early identification of patients at risk of AKI. We conducted a retrospective study using electronic health records of Singapore General Hospital emergency department patients who were admitted from 2008 to 2016. The primary outcome was inpatient AKI of any stage within 7 days of admission based on the Kidney Disease Improving Global Outcome (KDIGO) 2012 guidelines. A machine learning-based framework AutoScore was used to generate clinical scores from the study sample which was randomly divided into training, validation and testing cohorts. Model performance was evaluated using area under the curve (AUC). Among the 119,468 admissions, 10,693 (9.0%) developed AKI. 8491 were stage 1 (79.4%), 906 stage 2 (8.5%) and 1296 stage 3 (12.1%). The AKI Risk Score (AKI-RiSc) was a summation of the integer scores of 6 variables: serum creatinine, serum bicarbonate, pulse, systolic blood pressure, diastolic blood pressure, and age. AUC of AKI-RiSc was 0.730 (95% CI 0.714–0.747), outperforming an existing AKI Prediction Score model which achieved AUC of 0.665 (95% CI 0.646–0.679) on the testing cohort. At a cut-off of 4 points, AKI-RiSc had a sensitivity of 82.6% and specificity of 46.7%. AKI-RiSc is a simple clinical score that can be easily implemented on the ground for early identification of AKI and potentially be applied in international settings.

Scoring systems for the management of oncological hepato-pancreato-biliary patients

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

The future of healthcare is an organic blend of technology, innovation, and human connection. As artificial intelligence (AI) is gradually becoming a go-to technology in healthcare to improve efficiency and outcomes, we must understand our limitations. We should realize that our goal is not only to provide faster and more efficient care, but also to deliver an integrated solution to ensure that the care is fair and not biased to a group of sub-population. In this context, the field of cardio-cerebrovascular diseases, which encompasses a wide range of conditions-from heart failure to stroke-has made some advances to provide assistive tools to care providers. This article aimed to provide an overall thematic review of recent development focusing on various AI applications in cardio-cerebrovascular diseases to identify gaps and potential areas of improvement. If well designed, technological engines have the potential to improve healthcare access and equitability while reducing overall costs, diagnostic errors, and disparity in a system that affects patients and providers and strives for efficiency.

Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study

Clinical scoring systems have been developed for many specific applications, yet they remain underutilized for common reasons such as model inaccuracy and difficulty of use. For intensive care units specifically, the Acute Physiology and Chronic Health Evaluation (APACHE) score is used as a decision-making tool and hospital efficacy measure. In an attempt to alleviate the general underlying limitations of scoring instruments and demonstrate the utility of readily available medical databases, machine learning techniques were used to evaluate APACHE IV and IVa prediction measures in an open-source, teleICU research database. The teleICU database allowed for large-scale evaluation of APACHE IV and IVa predictions by comparing predicted values to the actual, recorded patient outcomes along with preliminary exploration of new predictive models for patient mortality and length of stay in both the hospital and the ICU. An increase in performance was observed in the newly developed models trained on the APACHE input variables highlighting avenues of future research and illustrating the utility of teleICU databases for model development and evaluation.

Medical calculators: Prevalence, and barriers to use

BACKGROUND AND OBJECTIVES

Medical calculators synthesize measurable evidence and help introduce new medical guidelines and standards. Some medical calculators can fulfill the role of CDS for Meaningful Use purposes. However, there are barriers for clinicians to use medical calculators in practice. Objectives of this study were to determine whether lack of EHR integration would be a barrier to use of medical calculators, and understand factors that may limit use and perceived usefulness of calculators METHODS: A survey about medical calculators as they relate to clinical efficiency, perceived usefulness, and barriers to effective use was conducted at a medium-sized academic medical center. 819 physicians were invited to participate in an online survey with a 13% response rate. Results were statistically analyzed to highlight factors related to use or non-use of medical calculators.

RESULTS

We found a negative correlation between use of medical calculators and years of experience (p < 0.001), with decreasing calculator use as experience goes up. Barriers to using medical calculators by non-users and users of medical calculators show that necessity and integration are significantly different with p < 0.001 and p = 0.037, respectively. 46.7% of non-users reported necessity as a barrier compared to 7.7% of users. Integration was reported as a barrier for 43.6% of users, but only 13.3% of non-users. 61% of users indicated that calculators made them more efficient, and 70% reported that unavailability of normally used calculators make them less efficient. 60% of users indicated that they are somewhat or very likely to use newly published medical calculators.

CONCLUSION

The results highlight that medical calculators are important for care delivery by both users and non-users. For non-users, they are seen as having a potentially positive impact on patient care, but unnecessary as part of clinical practice. For medical calculator users, calculators are an important part of regular workflow for efficiency improvement. Clinicians with fewer years of experience show an eagerness to consume newly published calculators, making these kinds of CDS a potentially useful way to disseminate new medical evidence. The survey results suggest that when medical calculators can be automated and integrated into the EHR as part of everyday workflow then efficiency and adoption may increase.