Prognostic model to identify and quantify risk factors for mortality among hospitalised patients with COVID-19 in the USA

Abstract

ObjectivesTo develop a prognostic model to identify and quantify risk factors for mortality among patients admitted to the hospital with COVID-19.DesignRetrospective cohort study. Patients were randomly assigned to either training (80%) or test (20%) sets. The training set was used to fit a multivariable logistic regression. Predictors were ranked using variable importance metrics. Models were assessed by C-indices, Brier scores and calibration plots in the test set.SettingOptum de-identified COVID-19 Electronic Health Record dataset including over 700 hospitals and 7000 clinics in the USA.Participants17 086 patients hospitalised with COVID-19 between 20 February 2020 and 5 June 2020.Main outcome measureAll-cause mortality while hospitalised.ResultsThe full model that included information on demographics, comorbidities, laboratory results, and vital signs had good discrimination (C-index=0.87) and was well calibrated, with some overpredictions for the most at-risk patients. Results were similar on the training and test sets, suggesting that there was little overfitting. Age was the most important risk factor. The performance of models that included all demographics and comorbidities (C-index=0.79) was only slightly better than a model that only included age (C-index=0.76). Across the study period, predicted mortality was 1.3% for patients aged 18 years old, 8.9% for 55 years old and 28.7% for 85 years old. Predicted mortality across all ages declined over the study period from 22.4% by March to 14.0% by May.ConclusionAge was the most important predictor of all-cause mortality, although vital signs and laboratory results added considerable prognostic information, with oxygen saturation, temperature, respiratory rate, lactate dehydrogenase and white cell count being among the most important predictors. Demographic and comorbidity factors did not improve model performance appreciably. The full model had good discrimination and was reasonably well calibrated, suggesting that it may be useful for assessment of prognosis.

Age differences in the association of comorbid burden with adverse outcomes in SARS-CoV-2

“…Consistent with earlier studies conducted in a range of different populations infected with SARS-CoV-2 [ 8 , 9 , 14 – 29 , 71 ], the age gradient for crude mortality was extremely steep and mortality rates increased exponentially while slopes for non-death outcomes plateaued at older ages. Age-related increases in rates of ICU admission and mechanical ventilation among members of our cohort were much less steep than those for mortality and hospitalization.…”

“…Older age is the strongest risk factor for infection with the severe acute respiratory virus syndrome coronavirus-2 (SARS-CoV-2) and older adults have been especially hard-hit during the pandemic [ 1 , 2 ]. Older adults with SARS-CoV-2 are at increased risk for hospitalization [ 3 – 5 ], critical illness [ 4 , 6 – 13 ], prolonged hospitalization [ 11 , 14 ] and mortality [ 8 , 9 , 14 – 29 ] compared with their younger counterparts. Because a disproportionate number of hospitalizations and deaths in patients with coronavirus disease 2019 (COVID-19) occur among older adults [ 30 – 32 ], the burden of SARS-CoV-2 has been greatest in countries [ 33 – 36 ] and communities [ 37 ] with older populations, and in healthcare facilities [ 38 – 40 ] and health systems serving older adults [ 41 ].…”

Socioeconomic position and the COVID-19 care cascade from testing to mortality in Switzerland: a population-based analysis

“…We did not find any association with positive tests per population (figure 3). The number of positive tests per number of tests decreased as Swiss-SEP group increased (adjusted IRR 0•97 [0•96-0•98]), corresponding to a 25% (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31) lower test positivity in the highest compared with the lowest socio economic group (figure 3). The greater uptake of testing in neighbourhoods of higher socioeconomic position masked the higher number of positive tests among individuals from lower SEP than in higher SEP neighbourhoods.…”

“…Early diagnosis of SARS-CoV-2 infections and adequate initial management might improve the prognosis of individuals with COVID-19, whereas prognosis is worse in patients diagnosed late, with low oxygen saturation and signs of pneumonia. 30 Rapid diagnosis and isolation are the key to preventing transmission; communities with higher testing levels will benefit from lower rates of transmission. The SARS-CoV-2 tests were a new technology and testing capacity was limited in Switzerland, particularly during the first wave of the pandemic.…”

Association of Frailty with Adverse Outcomes in Patients with Suspected COVID-19 Infection

“…Older age is associated with adverse outcomes in COVID-19 patients [1][2][3]. Frailty, on the other hand, appears to be a predictor for adverse outcomes in hospitalised COVID-19 patients [4][5][6][7].…”