key: cord-0844752-gcepkslt authors: Davis, John P.; Wessells, Dustin A.; Moorman, J. Randall title: Coronavirus Disease 2019 Calls for Predictive Analytics Monitoring—A New Kind of Illness Scoring System date: 2020-12-18 journal: Crit Care Explor DOI: 10.1097/cce.0000000000000294 sha: 7f3a8a1ee50efc16e0d39df96d75af7c8a61eded doc_id: 844752 cord_uid: gcepkslt Coronavirus disease 2019 can lead to sudden and severe respiratory failure that mandates endotracheal intubation, a procedure much more safely performed under elective rather than emergency conditions. Early warning of rising risk of this event could benefit both patients and healthcare providers by reducing the high risk of emergency intubation. Current illness severity scoring systems, which usually update only when clinicians measure vital signs or laboratory values, are poorly suited for early detection of this kind of rapid clinical deterioration. We propose that continuous predictive analytics monitoring, a new approach to bedside management, is more useful. The principles of this new practice anchor in analysis of continuous bedside monitoring data, training models on diagnosis-specific paths of deterioration using clinician-identified events, and continuous display of trends in risks rather than alerts when arbitrary thresholds are exceeded. T he coronavirus disease 2019 (COVID- 19) pandemic is a black swan event for the healthcare system. Overwhelmed hospitals may fail to meet community needs. Strained resources must be targeted to provide the sickest patients with the highest levels of care, while diverting others to outpatient protocols. Triage is imperative, and doctors face nightmarish decisions of allocating ventilators (1) . Only on the battlefield is it so important to gauge the illness severity and trajectory of multiple patients simultaneously. The first question we hear clinicians asking on arrival in a COVID-19 unit is, "Who is the sickest patient?" They ask because the illness can rapidly lead to lung failure, recognized in the COVID-specific Surviving Sepsis Guidelines that include the need for monitoring of incipient respiratory failure (2) . Remarkably, there is but a single Best Practice Statement: "In adults with COVID-19 receiving non-invasive positive-pressure ventilation or high-flow nasal cannula, we recommend close monitoring for worsening of respiratory status, and early intubation in a controlled setting if worsening occurs. " In the heart of this pandemic, what does "close monitoring for worsening of respiratory status" mean? Can we look to familiar illness severity scores for help? In 1981, Knaus et al (3) introduced the Acute Physiology and Chronic Health Evaluation (APACHE) score and, with it, the durably appealing idea that a single number could inform on how sick an ICU patient was. The score grew with the times, evolving from pencil and paper, a tedious look at the first 24 hours, and weights decided upon by experts to a computerized, automated product founded on statistical analyses of many patients. Indeed, 1985's APACHE-II (4) was more accurate than the Sequential Organ Failure Assessment score (5), Confusion, bUn, RR (respiratory rate), BP (blood pressure), age>65 score (6), and National Early Warning Score (7, 8) in assessing COVID-19 patients in the ICU of the Tongji Hospital in China (9, 10) . These scores, however, were not devised for illnesses like COVID-19 that can lead to rapidly accelerating lung failure. Most use only measurements made on the first day (11) . Their dependence on values that are measured only when a clinician thinks to, like vital signs or laboratory tests, makes them sluggish with respect to the pace of the disease. They allow the illness a headstart I: 805 II: 5,815 III: 17,440 IV: 110,558 1981-2003 L, VS, D, C, and GCS O, R I: 0-130 II: 0-71 III: 0-299 IV: 0-286 No trials (3, 4 that can be impossible to catch up to. Whatever advantage they offer in the calibrated synthesis of many kinds of information, they lose with their pace or lack of it. There are other misalignments. As shown in Table 1 , the targets that current scores are trained to detect are diffuse and include death (in the hospital [44] or for any cause up to a year later [29] ), cardiac arrest (31), sepsis (34) , septic shock in the ICU (24), hemorrhage (38, 45) , and readmission (44) . Their inputs are often intermittent, slowly moving, or static predictors. Their weighting of values and ranges is sometimes based on expert opinion from the pre-COVID-19 era. Their scoring ranges are often nonintuitive. Their impacts have often been untested even in non-COVID-19 settings. We note that trials that used triggered alerts rather than continuous displays have had, at best, mixed results (18, 19, 35, (46) (47) (48) . These were APACHE-like tools and statistical models based on measured values taken when clinicians thought they needed them. We live, though, in the era of Artificial Intelligence and Big Data, and the promise of clinical decision support for bedside clinicians based on automated mathematical analysis of streaming data is known to us all. In addition, continuous cardiorespiratory monitoring is readily available in every ICU and many acute care ward settings. We have the appealing opportunity to analyze mathematically the voluminous continuous cardiorespiratory monitoring data to detect early signs of patient deterioration. The effort to collect, store, and analyze the 150 MB of data per patient per day seems worth the result-a real-time continuous assessment of patient status. In the rapidly moving world of COVID-19 patients, this makes more clinical sense than awaiting the results of nurse visits and blood draws. In the exercise of predictive analytics monitoring, we seek pathophysiological signatures of illness (38, 49, 50) . In the canonical example of respiratory sinus arrhythmia (51), the well-known coupling of the heart rate and the respiratory rate by the vagus nerve has long been taken as an index of health. In this context, the signature of health is slowing of the heart rate during expiration and speeding during inspiration, and the signature of illness is their absence. Godin et al found reduced heart rate variability in volunteers injected with endotoxin (52) and concluded that systemic inflammation uncoupled the heart and lungs, and presumably uncoupled others, leading to multiple organ dysfunction syndrome (53) . A comprehensive modern view is that many organs are coupled in physiologic networks (54, 55) that can be modulated during sleep and illness. Signatures differ from illness to illness, from hospital unit to hospital unit, and across the spectrum of age. In septic neonatal ICU (NICU) premature infants, for example, we identified the unique signature of abnormal heart rate characteristics (reduced variability and transient decelerations) hours prior to clinical presentation (56) . A heart rate characteristic index (27) based on novel mathematical analytics (49, (57) (58) (59) led to a continuous display of the fold-increase in the risk of neonatal sepsis in the next 24 hours (26, 50, 60) . In the largest randomized trial in neonatology, the display led to a more than 20% relative reduction in death in nine NICUs (26), a durable effect (61) mostly attributable to a reduction in deaths from sepsis (62) . Although this illness signature holds for several neonatal illnesses, the same is not true for adults (38) . For example, the physiologic signature of acute respiratory acute failure differed from that of hemorrhage in adult ICUs. In addition, although these two illness signatures were similar in our medical and surgical ICUs, the signatures of sepsis in the two units differed-in the surgery ICU, sepsis presented more like respiratory failure, and in the medical ICU, more like circulatory shock. A display that we devised for other ICUs and wards-Continuous Monitoring of Event Trajectories-which reports two risks, an x,y plot of the 3-hour trajectory of the fold-increase in risk of a respiratory event as a function of the fold-increase in risk of a cardiovascular one, led to a 50% reduction of the rate of septic shock in a surgical and trauma ICU (37, 63) . On one of our hospital floors, the finding was the same-signatures of the most common reasons for patient deterioration leading to ICU transfer differed greatly from one another, and no single predictive model sufficed (64 the ICU transfer events did not outperform the strategy of using multiple models, each of which was tuned to clinical deterioration scenarios specific to a hospital ward. How should we monitor COVID-19 patients? Since the illness has physiologic features similar to other forms of viral sepsis (65) and acute respiratory distress syndrome (ARDS) (66), we might use predictive analytics monitoring models trained on patients who, on individual chart review, had sepsis using Surviving Sepsis Campaign criteria, or respiratory failure leading to emergent intubation as documented by procedure notes from attending anesthesiologists (38, 39) . We note the recent finding that cytokine levels in patients with COVID-19 plus ARDS are lower than those in patients with sepsis plus ARDS (67), consistent with the clinical picture of primary respiratory deterioration. We propose that it may be better to follow lung function than to follow the markers of systemic inflammation in the blood. Following lung function, like looking for signatures of illness, in our view requires continuous recording of organ function: the more highly resolved, the better. Pinsky et al recently demonstrated the additional information of noninvasive and invasive heart rate and waveform data in early detection of hemorrhage in pigs (68, 69) , affirming clinical studies (45, 70) . Heart rate analysis is directly applicable to clinical practice-each heartbeat sends an easily detected signal and allows for detailed analysis of long time-series of interbeat intervals using new and old mathematics (71, 72) . A wealth of techniques have been applied in the time domain (73) , frequency domain (74) , and nonlinear dynamical domain (57, 58) , and many machine learning tools from multivariable logistic regression (75) to artificial neural networks (76, 77) have long been used to combine the results. Authoritative sources (78, 79) and clinical users (60, 63) have outlined what is required of clinical decision support in the era of artificial intelligence and of predictive analytics monitoring ( Table 2 ). In the table, we propose how the new continuous predictive analytics monitoring systems can realize these requirements. Here, we add four principles that we believe to be of equally paramount importance to an effective monitoring system. for rapidly moving illnesses should incorporate continuous cardiorespiratory monitoring in the ICU and on the floor when it is available, because it adds information to nurse-charted vital signs and laboratory tests (45, (68) (69) (70) . 2) Predictive analytics monitoring models should be trained on specific targets, because there is no one-size-fits-all model (38, 64) . 3) Clinical events that are used for training predictive analytics monitoring models should be identified by clinicians, because they are more accurate than computer searches of clinical databases (70, (83) (84) (85) (86) (87) . 4) These new kinds of clinical information require new tools and methods for implementation and integration (60, 63, 88) . All of these elements are directly relevant to the problem of COVID-19 respiratory failure. First, patients presenting for acute flu-like illnesses have diagnoses ranging from common viral infection to potentially catastrophic COVID-19 respiratory failure. Just as high-risk scores might predict severe illness and lead to admission to a hospital floor or ICU (40), low-risk scores might predict benign courses and identify patients who can be treated at home. Second, COVID-19 patients admitted to wards can benefit from prediction of rapid, severe pulmonary failure occurring several days into the illness. Third, COVID-19 patients in ICUs treated noninvasively might benefit if predictive monitoring shows risk, allowing them to avoid intubation as they begin to improve on their own. In addition, novel therapies like the antiviral remdesivir and the interleukin-6 receptor antagonist tocilizumab are precious resources and should be reserved for the patients predicted to be at most need. Predictive analytics monitoring can help identify them before the illness is too far advanced. Finally, the illness is very fast-moving, and there is an urgent need to know if patients respond to a course of therapy so that a failing therapy can be quickly stopped and new ones substituted. To conclude, COVID-19 infection-like other subacute potentially catastrophic illness-can cause rapid clinical deterioration for which early detection might improve outcomes. Volitional measurements of vital signs and labs can come too late. Predictive analytics monitoring that incorporates continuous cardiorespiratory monitoring data and uses targeted analytics that detect specific signatures of individual illnesses fit the clinical need better. Like all clinical decision support, effective predictive analytics monitoring requires intuitive and actionable displays of patient trajectories. It is time to advance these modern tools to the bedside. Adult ICU triage during the coronavirus disease 2019 pandemic: Who will live and who will die? Recommendations to improve survival Surviving sepsis campaign: Guidelines on the management of critically ill adults with coronavirus disease 2019 (COVID-19) APACHE-acute physiology and chronic health evaluation: A physiologically based classification system APACHE II: A severity of disease classification system The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine Defining community acquired pneumonia severity on presentation to hospital: An international derivation and validation study Standardising the Assessment of Acute-Illness Severity in the NHS The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death Acute physiology and chronic health evaluation II score as a predictor of hospital mortality in patients of coronavirus disease 2019 NEWS can predict deterioration of patients with COVID-19 Prediction models for diagnosis and prognosis of covid-19 infection: Systematic review and critical appraisal The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit* An electronic tool for the evaluation and treatment of sepsis in the ICU: A randomized controlled trial Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis European Sepsis Study Group: Systemic inflammatory response and progression to severe sepsis in critically ill infected patients A randomised controlled trial of the effect of continuous electronic physiological monitoring on the adverse event rate in high risk medical and surgical patients Cardiorespiratory instability before and after implementing an integrated monitoring system Integrated monitoring and analysis for early warning of patient deterioration Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: A randomised clinical trial Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: A prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals A computational approach to early sepsis detection A targeted real-time early warning score (TREWScore) for septic shock LiSep LSTM: A machine learning algorithm for early detection of septic shock Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: A randomized trial Abnormal heart rate characteristics preceding neonatal sepsis and sepsis-like illness Next generation patient monitor powered by in-silico physiology Development and validation of a continuous measure of patient condition using the electronic medical record A trial of a real-time alert for clinical deterioration in patients hospitalized on general medical wards Derivation of a cardiac arrest prediction model using ward vital signs* Multicenter development and validation of a risk stratification tool for ward patients Early warning scoring systems versus standard observations charts for wards in South Africa: A cluster randomized controlled trial An interpretable machine learning model for accurate prediction of sepsis in the ICU Automated identification of adults at risk for in-hospital clinical deterioration Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU Impact of predictive analytics based on continuous cardiorespiratory monitoring in a surgical and trauma intensive care unit Signatures of subacute potentially catastrophic illness in the ICU: Model development and validation Predictive Monitoring in Patients With Trauma (PreMPT) Group: Predicting the need for urgent intubation in a surgical/trauma intensive care unit Dynamic data in the ED predict requirement for ICU transfer following acute care admission Identifying the low risk patient in surgical intensive and intermediate care units using continuous monitoring Predictive analytics in the pediatric intensive care unit for early identification of sepsis: Capturing the context of age Developing the surveillance algorithm for detection of failure to recognize and treat severe sepsis Scalable and accurate deep learning with electronic health records Hemorrhage prediction models in surgical intensive care: Bedside monitoring data adds information to lab values Using continuous vital sign monitoring to detect early deterioration in adult postoperative inpatients Admitting acute ischemic stroke patients to a stroke care monitoring unit versus a conventional stroke unit: A randomized pilot study A machine learning algorithm to predict severe sepsis and septic shock: Development, implementation, and impact on clinical practice Cardiovascular oscillations at the bedside: Early diagnosis of neonatal sepsis using heart rate characteristics monitoring Using what you get: Dynamic physiologic signatures of critical illness Respiratory sinus arrhythmia: Noninvasive measure of parasympathetic cardiac control Experimental human endotoxemia increases cardiac regularity: Results from a prospective, randomized, crossover trial Uncoupling of biological oscillators: A complementary hypothesis concerning the pathogenesis of multiple organ dysfunction syndrome Network physiology reveals relations between network topology and physiological function Network physiology: How organ systems dynamically interact Toward the early diagnosis of neonatal sepsis and sepsis-like illness using novel heart rate analysis Physiological time-series analysis using approximate entropy and sample entropy Sample entropy analysis of neonatal heart rate variability Sample asymmetry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome Diffusing an innovation: Clinician perceptions of continuous predictive analytics monitoring in intensive care Mortality and neurodevelopmental outcomes in the heart rate characteristics monitoring randomized controlled trial Septicemia mortality reduction in neonates in a heart rate characteristics monitoring trial Advancing continuous predictive analytics monitoring: Moving from implementation to clinical action in a learning health system Early detection of inpatient deterioration: One prediction model does not fit all Consideration of severe coronavirus disease 2019 as viral sepsis and potential use of immune checkpoint inhibitors COVID-19-associated acute respiratory distress syndrome: Is a different approach to management warranted? Cytokine levels in critically ill patients with COVID-19 and other conditions Increasing cardiovascular data sampling frequency and referencing it to baseline improve hemorrhage detection Parsimony of hemodynamic monitoring data sufficient for the detection of hemorrhage Cardiorespiratory dynamics measured from continuous ECG monitoring improves detection of deterioration in acute care patients: A retrospective cohort study Heart Rate Variability: Standards of Measurement, Physiological Interpretation and Clinical Use: Task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology An open source benchmarked toolbox for cardiovascular waveform and interval analysis Decreased heart rate variability and its association with increased mortality after acute myocardial infarction Power spectrum analysis of heart rate fluctuation: A quantitative probe of beat-to-beat cardiovascular control Logistic and Ordinal Regression, and Survival Analysis (Springer Series in Statistics). Second Edition Machine learning in medicine The dynamic range of neonatal heart rate variability Clinical decision support in the era of artificial intelligence Optimizing Strategies for Clinical Decision Support: Summary of a Meeting Series Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement Development and reporting of prediction models: Guidance for authors from editors of respiratory, sleep, and critical care journals Minimum information about clinical artificial intelligence modeling: The MI-CLAIM checklist CDC Prevention Epicenter Program: Incidence and trends of sepsis in US Hospitals using clinical vs claims data Identifying patients with severe sepsis using administrative claims: Patient-level validation of the angus implementation of the international consensus conference definition of severe sepsis Functional outcomes of general medical patients with severe sepsis Validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) screening for sepsis in surgical mortalities Accuracy of administrative data for surveillance of healthcare-associated infections: A systematic review iHealth Project Team: Techniques to aid the implementation of novel clinical information systems: A systematic review We thank Jessica Keim-Malpass, Rebecca Kitzmiller, and Liza Prudente Moorman for suggestions and insights about implementation of predictive analytics monitoring. We also thank our colleagues in the Center for Advanced Medical Analytics, in particular Prof. DE Lake, Sepsis Challenger, for many discussions and insights on predictive analytics monitoring.