Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19

Abstract

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, and the capacity of intensive care units was a limiting factor during the peak of the pandemic and is generally dependent on a country's clinical resources. Purpose To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. Materials and Methods In this retrospective study, which included patients from March 7, 2020, to April 24, 2020, a consecutive cohort of hospitalized patients with real-time reverse transcription polymerase chain reaction-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (ie, death and/or intensive care unit admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, chest radiographic, and laboratory findings. Distribution and severity of lung involvement were visually assessed by using an eight-point scale (chest radiography score). Internal validation was performed by using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve. Decision curve analysis was performed, and a risk calculator was derived. Results The cohort included 356 hospitalized patients (mean age, 69 years ± 12 [standard deviation]; 237 men) of whom 168 (47%) developed critical illness. The final risk model's variables included sex, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease, and chest radiography score at hospital presentation. The area under the receiver operating characteristic curve of the model was 0.77 (95% CI: 0.72, 0.81; P < .001). A risk calculator was derived for individual risk assessment: Dutch COVID-19 risk model. At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness, of which 59 (83%) would be true-positive results. Conclusion A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of intensive care unit beds or facilities. © RSNA, 2020 Online supplemental material is available for this article.

Figure 1:

Flowchart of patient inclusion. Patients without or with a negative RT-PCR, patients who were not hospitalized, and a small number of patients who did not receive a chest radiograph or were transferred from another hospital without a clear onset of symptoms were excluded. A total of 356 patients were eligible for this study.

Figure 2:

Chest radiography scoring: in a 50-year-old patient with RT-PCR proven coronavirus disease 2019 who was hospitalized but not admitted to the ICU. Chest radiography scoring was as follows: right upper lung zone mild/moderate involvement (1 point), the right lower lung zone mild/moderate involvement (1 points), as well as for the left upper and lower lung zones mild/moderate involvement (both 1 point), resulting in a cumulative score of 4.

Figure 3:

Receiver operating characteristics curve for the multivariable logistic regression model predicting critical illness in patients with COVID-19. The model including gender, clinical, laboratory, and imaging (chest radiography) parameters (n=356) reached an AUC of 0.77 for prediction of critical illness.

Figure 4:

Decision curve for the Dutch COVID-19 risk model. The gray and black lines (horizontal) represent the scenarios where all or none of the hospitalized patients would be prospectively determined by the risk model, respectively. The red line demonstrates the net benefit of the risk model dependent at the chosen risk threshold. The accompanying thinner lines represent the 95% confidence intervals.

Figure 5:

Examples of patients with coronavirus disease 2019 who did (i.e. required intubation and or died) and did not develop critical illness (i.e. did not need intubation and were discharged). A, Radiograph in an 80-year-old woman with 7 days of symptoms prior to emergency ward presentation. She had hypertension and chronic obstructive lung disease as comorbidity. Laboratory findings were as follows: neutrophil granulocytes 6.4x10⁹/L, C-reactive protein 282 mg/L, lactate dehydrogenase 335 IU/L. On chest radiography she exhibited diffuse bilateral opacities and a chest radiography score of 4. The calculated risk score was 0.69. She developed critical illness and died 4 days after hospital admission. B, Radiograph in a 46-year-old woman with no comorbidities had 9 days of symptoms prior to emergency ward presentation. Laboratory findings were as follows: neutrophil granulocytes 3.8x10⁹/L, C-reactive protein 29 mg/L, lactate dehydrogenase 429 IU/L. Chest radiography showed bilateral peripheral opacities and a chest radiography score of 5. The calculated risk score was 0.24. She was not admitted to the ICU and successfully discharged after 2 days of hospitalization.