Article Figures & Tables
Figures
- Figure 1:
Electronic medical records used for model development. The date of diagnosis of SARS-CoV-2 infection is used as the index date. From this date, a look-ahead period of 30 days is used to look for the outcome of hospitalization related to COVID-19. Besides including demographic information, independent predictor variables were constructed by aggregating 2 years of medical records (e.g., past health care utilization, laboratory results and drug prescriptions) up to 30 days before the index date. The complete list of predictor variables calculated can be found in Appendix 1, Supplementary Table 1 (available at www.cmajopen.ca/content/9/4/E1223/suppl/DC1). The icons used in this figure are freely available at www.flaticon.com and were downloaded from this site on Jan. 17, 2021.
- Figure 2:
Flow diagram of study cohort (derivation and validation). The ICES COVID-19 cohort was last updated on Nov. 7, 2020, and it includes patients with index (diagnosis) dates between Feb. 2, 2020, and Nov. 5, 2020. Patients with an index date after Oct. 5, 2020, or currently living in a long-term care facility were excluded. Included patients were followed up for 30 days for the outcome of hospitalization for COVID-19.
- Figure 3:
Extreme Gradient Boosting (XGBoost) model performance. The final model was trained with 18 features extracted from the ICES COVID-19 data source. (A) The blue line shows the receiver operating characteristic curve. (B) Calibration curve of the final XGBoost model on the validation data set, where each blue dot (bins in the histogram) corresponds to a decile of predicted risk.
- Figure 4:
Comparison of recall at top percentiles. The final Extreme Gradient Boosting (XGBoost) model recall (percentage of true hospitalizations recovered in validation data set) was compared against 4 empirical rules.
Tables
Characteristic No. (%) of patients* Standardized difference (hospitalized – not hospitalized) All patients
n = 36 323Hospitalized
n = 2583Not hospitalized
n = 33 740Age, yr, median (IQR) 45 (31–58) 64 (54–77) 43 (30–56) 1.175 No. of comorbidities, median (IQR)† 1 (0–3) 3 (2–6) 1 (0–3) 0.935 Male 17 428 (48.0) 1453 (56.3) 15 975 (47.3) 0.179 Female 18 895 (52.0) 1130 (43.7) 17 765 (52.7) −0.179 Asthma 5460 (15.0) 480 (18.6) 4980 (14.8) 0.103 Cancer 1453 (4.0) 297 (11.5) 1156 (3.4) 0.311 Chronic heart failure 831 (2.3) 275 (10.6) 556 (1.6) 0.381 COPD 1959 (5.4) 457 (17.7) 1502 (4.5) 0.432 Diabetes 5273 (14.5) 940 (36.4) 4333 (12.8) 0.568 Hypertension 8994 (24.8) 1477 (57.2) 7517 (22.3) 0.763 Hospitalized for COVID-19 2583 (7.1) 2583 (100) 0 (0) NA Died from COVID-19 906 (2.5) 543 (21.0) 364 (1.1) 0.67 Note: COPD = chronic obstructive pulmonary disease, IQR = interquartile range, NA = not applicable.
↵* Unless otherwise stated.
↵† The variable “no. of comorbidities” accounts for the following conditions (see Model development under Methods): acute myocardial infarction, arrhythmia, arthritis, asthma, cancer, chronic heart failure, colitis, COPD, coronary disease, diabetes, hypertension, osteoarthritis, osteoporosis and kidney disease.
Characteristic No. (%) of patients* Standardized difference Development set
n = 29 058Validation set
n = 7265Age, yr, median (IQR) 44 (31–58) 45 (31–58) −0.015 No. of comorbidities, median (IQR)† 1 (0–3) 1 (0–3) −0.009 Male 13 995 (48.2) 3433 (47.3) 0.018 Female 15 063 (51.8) 3832 (52.7) 0.003 Asthma 4376 (15.1) 1084 (14.9) 0.004 Cancer 1163 (4.0) 290 (4.0) 0.001 Chronic heart failure 668 (2.3) 163 (2.2) 0.004 COPD 1549 (5.3) 410 (5.6) −0.014 Diabetes 4202 (14.5) 1071 (14.7) −0.008 Hypertension 7181 (24.7) 1813 (25.0) −0.006 Hospitalized for COVID-19 2043 (7.0) 540 (7.4) −0.016 Died from COVID-19 719 (2.5) 187 (2.6) −0.006 Note: COPD = chronic obstructive pulmonary disease, IQR = interquartile range.
↵* Unless otherwise stated.
↵† The variable “no. of comorbidities” accounts for the following conditions (see Model development under Methods): acute myocardial infarction, arrhythmia, arthritis, asthma, cancer, chronic heart failure, colitis, COPD, coronary disease, diabetes, hypertension, osteoarthritis, osteoporosis and kidney disease.
Predictor variable SHAP value* Age 0.7567 Days since last creatinine blood test 0.1320 Geographical latitude 0.1299 Days since last basophils test 0.1196 Male 0.1196 No. of family doctor visits in the last 2 yr 0.1165 No. of comorbidities 0.1072 No. of unique drug subclasses taken in the last 2 yr 0.0845 Highest recorded level of creatinine in the last 2 yr 0.0773 No. of diagnostic radiology studies in the last 2 yr 0.0381 Average measurement of neutrophils in blood in the last 2 yr 0.0289 No. of doctor visits in the last 2 yr 0.0237 Median level of neutrophils in the last 2 yr 0.0165 Average level of leukocytes in the last 2 yr 0.0144 No. of creatinine tests in the last 2 yr 0.0144 Highest recorded level of hemoglobin in blood in the last 2 yr 0.0021 History of chronic kidney disease 0.0021 Days since last mean corpuscular hemoglobin test in the last 2 yr 0.0010 Note: SHAP = Shapley Additive Explanation, XGBoost = Extreme Gradient Boosting.
↵* SHAP values represent the weighted average of marginal contributions for each predictive variable included in the XGBoost model.
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