Development of a risk score to identify patients at high risk for a severe course of COVID-19

Aim

We aimed to develop a risk score to calculate a person's individual risk for a severe COVID-19 course (POINTED score) to support prioritization of especially vulnerable patients for a (booster) vaccination.

Subject and methods

This cohort study was based on German claims data and included 623,363 individuals with a COVID-19 diagnosis in 2020. The outcome was COVID-19 related treatment in an intensive care unit, mechanical ventilation, or death after a COVID-19 infection. Data were split into a training and a test sample. Poisson regression models with robust standard errors including 35 predefined risk factors were calculated. Coefficients were rescaled with a min-max normalization to derive numeric score values between 0 and 20 for each risk factor. The scores' discriminatory ability was evaluated by calculating the area under the curve (AUC).

Results

Besides age, down syndrome and hematologic cancer with therapy, immunosuppressive therapy, and other neurological conditions were the risk factors with the highest risk for a severe COVID-19 course. The AUC of the POINTED score was 0.889, indicating very good predictive validity.

Conclusion

The POINTED score is a valid tool to calculate a person's risk for a severe COVID-19 course.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10389-023-01884-7.

Post-COVID-19-associated morbidity in children, adolescents, and adults: A matched cohort study including more than 157,000 individuals with COVID-19 in Germany

BACKGROUND

Long-term health sequelae of the Coronavirus Disease 2019 (COVID-19) are a major public health concern. However, evidence on post-acute COVID-19 syndrome (post-COVID-19) is still limited, particularly for children and adolescents. Utilizing comprehensive healthcare data on approximately 46% of the German population, we investigated post-COVID-19-associated morbidity in children/adolescents and adults.

METHODS AND FINDINGS

We used routine data from German statutory health insurance organizations covering the period between January 1, 2019 and December 31, 2020. The base population included all individuals insured for at least 1 day in 2020. Based on documented diagnoses, we identified individuals with polymerase chain reaction (PCR)-confirmed COVID-19 through June 30, 2020. A control cohort was assigned using 1:5 exact matching on age and sex, and propensity score matching on preexisting medical conditions. The date of COVID-19 diagnosis was used as index date for both cohorts, which were followed for incident morbidity outcomes documented in the second quarter after index date or later.Overall, 96 prespecified outcomes were aggregated into 13 diagnosis/symptom complexes and 3 domains (physical health, mental health, and physical/mental overlap domain). We used Poisson regression to estimate incidence rate ratios (IRRs) with 95% confidence intervals (95% CIs). The study population included 11,950 children/adolescents (48.1% female, 67.2% aged between 0 and 11 years) and 145,184 adults (60.2% female, 51.1% aged between 18 and 49 years). The mean follow-up time was 236 days (standard deviation (SD) = 44 days, range = 121 to 339 days) in children/adolescents and 254 days (SD = 36 days, range = 93 to 340 days) in adults. COVID-19 and control cohort were well balanced regarding covariates. The specific outcomes with the highest IRR and an incidence rate (IR) of at least 1/100 person-years in the COVID-19 cohort in children and adolescents were malaise/fatigue/exhaustion (IRR: 2.28, 95% CI: 1.71 to 3.06, p < 0.01, IR COVID-19: 12.58, IR Control: 5.51), cough (IRR: 1.74, 95% CI: 1.48 to 2.04, p < 0.01, IR COVID-19: 36.56, IR Control: 21.06), and throat/chest pain (IRR: 1.72, 95% CI: 1.39 to 2.12, p < 0.01, IR COVID-19: 20.01, IR Control: 11.66). In adults, these included disturbances of smell and taste (IRR: 6.69, 95% CI: 5.88 to 7.60, p < 0.01, IR COVID-19: 12.42, IR Control: 1.86), fever (IRR: 3.33, 95% CI: 3.01 to 3.68, p < 0.01, IR COVID-19: 11.53, IR Control: 3.46), and dyspnea (IRR: 2.88, 95% CI: 2.74 to 3.02, p < 0.01, IR COVID-19: 43.91, IR Control: 15.27). For all health outcomes combined, IRs per 1,000 person-years in the COVID-19 cohort were significantly higher than those in the control cohort in both children/adolescents (IRR: 1.30, 95% CI: 1.25 to 1.35, p < 0.01, IR COVID-19: 436.91, IR Control: 335.98) and adults (IRR: 1.33, 95% CI: 1.31 to 1.34, p < 0.01, IR COVID-19: 615.82, IR Control: 464.15). The relative magnitude of increased documented morbidity was similar for the physical, mental, and physical/mental overlap domain. In the COVID-19 cohort, IRs were significantly higher in all 13 diagnosis/symptom complexes in adults and in 10 diagnosis/symptom complexes in children/adolescents. IRR estimates were similar for age groups 0 to 11 and 12 to 17. IRs in children/adolescents were consistently lower than those in adults. Limitations of our study include potentially unmeasured confounding and detection bias.

CONCLUSIONS

In this retrospective matched cohort study, we observed significant new onset morbidity in children, adolescents, and adults across 13 prespecified diagnosis/symptom complexes, following COVID-19 infection. These findings expand the existing available evidence on post-COVID-19 conditions in younger age groups and confirm previous findings in adults.

TRIAL REGISTRATION

ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT05074953.

Covid-19 risk factors: statistical learning from German healthcare claims data

BACKGROUND

Precise individual risk quantification of severe courses of Covid-19 is needed to prioritize protective measures and to assess population risks in a phase of increased immunization. So far, results for the German population are lacking. Furthermore, existing studies pre-specify comorbidity risks by broad categories rather than deriving them from the data using statistical learning techniques.

METHODS

Risk factors for severe, critical and lethal courses of Covid-19 are identified from a large German claims dataset covering more than 4 million individuals. To avoid prior grouping and pre-selection of risk factors, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. These are processed using a LASSO approach.

RESULTS

We identify relevant risk factors, among which hypertensive diseases, heart disease and the corresponding medications are most relevant at population level. Prior use of diuretics is the strongest single medical predictor for severe course (e.g. Torasemide, odds ratio (OR) 1.801), but also for a critical course (OR 2.304) and death (OR 2.523). To assess risk profiles at the individual level, our approach sums up many such factors and has better predictive ability than using pre-specified morbidity groups (AUC for predicting critical course 0.875 versus AUC ≤ 0.865).

CONCLUSIONS

The proposed method can help to identify risk factors and assess risk at the individual level for other infectious diseases. The results can be used by administrative data holders to guide protective policies, while a risk index can be applied in clinical studies with a narrower focus.