Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation.

OBJECTIVES: We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients. DESIGN: Retrospective analysis leading to model development and validation. SETTING: All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record. PATIENTS: Eleven thousand eight hundred forty-seven ICU patient admissions. INTERVENTIONS: The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model. MEASUREMENTS AND MAIN RESULTS: Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81). CONCLUSIONS: We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

Stochastic modeling of central apnea events in preterm infants.

A near-ubiquitous pathology in very low birth weight infants is neonatal apnea, breathing pauses with slowing of the heart and falling blood oxygen. Events of substantial duration occasionally occur after an infant is discharged from the neonatal intensive care unit (NICU). It is not known whether apneas result from a predictable process or from a stochastic process, but the observation that they occur in seemingly random clusters justifies the use of stochastic models. We use a hidden-Markov model to analyze the distribution of durations of apneas and the distribution of times between apneas. The model suggests the presence of four breathing states, ranging from very stable (with an average lifetime of 12 h) to very unstable (with an average lifetime of 10 s). Although the states themselves are not visible, the mathematical analysis gives estimates of the transition rates among these states. We have obtained these transition rates, and shown how they change with post-menstrual age; as expected, the residence time in the more stable breathing states increases with age. We also extrapolated the model to predict the frequency of very prolonged apnea during the first year of life. This paradigm-stochastic modeling of cardiorespiratory control in neonatal infants to estimate risk for severe clinical events-may be a first step toward personalized risk assessment for life threatening apnea events after NICU discharge.

Cost of a recall of a single-center experience managing the Riata defibrillator lead.

Riata and Riata ST defibrillator leads (St. Jude Medical, Sylmar, California) were recalled in 2011 due to increased risk of insulation failure leading to externalized cables. Fluoroscopic screening can identify insulation failure, although the relation between mechanical failure and electrical failure is unclear. At the time of the recall, the University of Virginia developed a screening program, including fluoroscopic evaluation, education sessions, device interrogation, and remote monitoring for patients with this defibrillator lead. The aim of this study was to review the outcomes of the screening program, including costs, which were absorbed by our institution. Costs were calculated using Medicare reimbursement estimates. Forty-eight patients participated in the screening program. At initial screening, 31% were found to have evidence of insulation failure but electrical function was normal in all leads. The cost of this program was $35,358.72. The cost per diagnosis of mechanical lead failure was $2,357.25. During 2 years of follow-up, 1 patient experienced Riata lead electrical failure without fluoroscopic evidence of insulation failure. Patients were more likely to have a lead revision if there was evidence of insulation failure. Lead revisions occurred at the time of generator change in 88% of patients with insulation failure but in only 14% of patients with a fluoroscopically normal lead (p = 0.04). The cost of recall-related defibrillator lead revisions was $81,704.55. In conclusion, our Riata screening program added expense without clear benefit to patients. In fact, patients may have been put at more risk by undergoing defibrillator lead revisions based solely on the results of the fluoroscopic screening.