Identification on Admission of COVID-19 Patients at Risk of Subsequent Rapid Clinical Deterioration

Abstract

Introduction: Recent localized surges in COVID-19 cases have resulted in the hospitals serving those areas being overwhelmed. In such cases, the ability to rapidly and objectively determine a patient's acuity and predict near-term care needs is a major challenge. At issue is the clinician's ability to correctly identify patients at risk for subsequent rapid clinical deterioration. Data-driven tools that can support such determinations in real-time may be a valuable adjunct to clinician judgement during COVID-19 surges. Objective: To assess the effectiveness of the Rothman Index (RI) predictive model in distinguishing the risk of subsequent deterioration or elevated care needs among hospitalized COVID-19 patients at the time of hospital admission. Methods: We evaluated the initial RI score on admission to predict COVID-19 patient risk for 216 COVID-19 patients discharged from March 21st to June 7th, 2020 at Sinai LifeBridge Hospital and 1,453 COVID-19 patients discharged from any of Yale New Haven Health System's Yale New Haven, Bridgeport, and Greenwich hospitals from April 1st to April 28th, 2020. In-hospital mortality as a function of age and RI on admission for COVID-19 and non-COVID-19 patients were compared. AUC values using each COVID-19 patient's initial RI on admission to predict in-hospital mortality, mechanical ventilation, and ICU utilization were computed, as were precision and recall for mortality prediction at specific RI thresholds. Results: The RI computed at the time of admission provides a high degree of objective discrimination to differentiate the COVID-19 population into high and low risk populations at the outset of hospitalization. The high risk segment based on initial RI constitutes 20-30% of the COVID-19 positive population with mortality rates from 40-50%. The low risk segment based on initial RI constitutes 40%-55% of the population with mortality rates ranging from 1%-8%. Of note is that COVID-19 patients who present with heightened but generally unremarkable acuity can be identified early as having considerably elevated risk for subsequent physiological deterioration. Conclusion: COVID-19 patients exhibit elevated mortality rates compared to non-COVID-19 medical service patients and may be subject to rapid deterioration following hospital admission. A lack of predictive indicators for identifying patients at high risk of subsequent deterioration or death can pose a challenge to clinicians. The RI has excellent performance characteristics when stratifying risk among COVID-19 patients at the time of admission. The RI can assist clinicians in real-time with a high degree of objective discrimination by segmenting the COVID-19 population into high and low risk populations. This supports rapid and optimal patient bed assignment and resource allocation.

Evaluation of Clinical Predictors for Major Outcomes in Patients Hospitalized With COVID-19: The Potential Role of the Rothman Index

“…A recent study conducted by PeraHealth, the developers of RI, has shown that RI has some use in risk stratification for patients with COVID-19; however, the accuracy and effectiveness of its implementation are of unknown significance [ 13 ].…”

Development of a repeated-measures predictive model and clinical risk score for mortality in ventilated COVID-19 patients

“…Candidate predictors (i.e., laboratory, demographic, and clinical parameters) were selected from existing models and published literature 6,7,9,11,12,20,21 and included age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE II) score at the time of ICU admission, 22 respiratory rate, oxygen requirements and saturation, temperature at admission, white blood cell count and lymphopenia, end-organ dysfunction (including acute kidney injury and evidence of hepatic injury through elevations of hepatic enzymes), and preadmission patient comorbidities (e.g., hypertension, diabetes, coronary artery disease, current smoker, pulmonary disease). Data on biomarkers such as albumin, procalcitonin, ferritin, d-dimer, C-reactive protein (CRP), lactate dehydrogenase, and erythrocyte sedimentation rate were collected where available.…”