Derivation of a Clinical Risk Score to Predict 14-Day Occurrence of Hypoxia, ICU Admission, and Death Among Patients with Coronavirus Disease 2019

Abstract

Background Uncertainty surrounding COVID-19 regarding rapid progression to acute respiratory distress syndrome and unusual clinical characteristics make discharge from a monitored setting challenging. A clinical risk score to predict 14-day occurrence of hypoxia, ICU admission, and death is unavailable. Objective Derive and validate a risk score to predict suitability for discharge from a monitored setting among an early cohort of patients with COVID-19. Design Model derivation and validation in a retrospective cohort. We built a manual forward stepwise logistic regression model to identify variables associated with suitability for discharge and assigned points to each variable. Event-free patients were included after at least 14 days of follow-up. Participants All adult patients with a COVID-19 diagnosis between March 1, 2020, and April 12, 2020, in 10 hospitals in Massachusetts, USA. Main Measures Fourteen-day composite predicting hypoxia, ICU admission, and death. We calculated a risk score for each patient as a predictor of suitability for discharge evaluated by area under the curve. Key Results Of 2059 patients with COVID-19, 1326 met inclusion. The 1014-patient training cohort had a mean age of 58 years, was 56% female, and 65% had at least one comorbidity. A total of 255 (25%) patients were suitable for discharge. Variables associated with suitability for discharge were age, oxygen saturation, and albumin level, yielding a risk score between 0 and 55. At a cut point of 30, the score had a sensitivity of 83% and specificity of 82%. The respective c-statistic for the derivation and validation cohorts were 0.8939 (95% CI, 0.8687 to 0.9192) and 0.8685 (95% CI, 0.8095 to 0.9275). The score performed similarly for inpatients and emergency department patients. Conclusions A 3-item risk score for patients with COVID-19 consisting of age, oxygen saturation, and an acute phase reactant (albumin) using point of care data predicts suitability for discharge and may optimize scarce resources.

Call, chosen, HA2T2, ANDC: validation of four severity scores in COVID-19 patients

“…The scores discussed here all use no more than four variables that are relatively readily available in middle- to high-income countries. There also exists a simplified version of the CHOSEN score that does not rely on laboratory values but did also not perform as well in the original cohort [ 11 ].…”

“…Disease progression was defined as respiratory rate ≥ 30 breaths per minute (bpm), peripheral oxygen saturation (SpO 2 ) ≤ 93%, arterial partial oxygen pressure (PaO 2 )/fraction of inspired oxygen (FiO 2 ) ≤ 300 mmHg, mechanical ventilation or worsening of lung computer tomography (CT) findings [ 10 ]. The CHOSEN score used age, FiO 2 and albumin to predict progression defined as requiring supplemental oxygen, admission to the intensive care unit (ICU) or death [ 11 ]. The authors reported a good discriminative capacity for their score with an AUC of 0.89 (95%-CI 0.87–0.91) in their derivation and 0.87 (95%-CI 0.81–0.93) in their validation cohort [ 11 ].…”

“…The CHOSEN score used age, FiO 2 and albumin to predict progression defined as requiring supplemental oxygen, admission to the intensive care unit (ICU) or death [ 11 ]. The authors reported a good discriminative capacity for their score with an AUC of 0.89 (95%-CI 0.87–0.91) in their derivation and 0.87 (95%-CI 0.81–0.93) in their validation cohort [ 11 ]. The HA 2 T 2 score was used to predict all-cause in-hospital mortality in COVID-19 patients based on need for supplemental oxygen, age and troponin [ 12 ].…”

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…Other works presenting solutions from the MLDA domain can be consulted in Kirby et al [71], Joshi et al [69], and Van Klaveren et al [95] who performed a logistic regression model to identify variables associated with discharge appropriateness and optimization of healthcare ED resources during the pandemic. Other useful single-approached proposals can be found in Alfaro-Martinez et al [43], Freund et al [61], Brendish et al [49], Esposito et al [59], Gordon et al [66], Levine et al [74], Carlile et al [51], and García de Guadiana-Romualdo et al [63].…”

Development and Validation of a Novel Risk Score for Predicting Clinical Deterioration in COVID-19 Patients: The ABCD Risk Score

“…Several biomarkers of clinical deterioration in COVID-19 have been identi ed, such as complete blood count (lymphocyte count (4) and neutrophil-to-lymphocyte ratio [NLR] (5)), biochemical tests (blood urea nitrogen [BUN] (6, 7) and lactate dehydrogenase [LDH] (8)), blood in ammatory markers (C-reactive protein [CRP], interleukin-6 [IL-6] (9, 10), and ferritin (11)), and blood coagulation tests (D-dimer (12)). Scoring models for risk strati cation have been developed using biomarkers, clinical signs, and imaging ndings (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28). However, most reported prediction models are considered to have a high risk of bias and overestimation of their reported performance due to de cient temporal and external validation (29).…”