TY - JOUR
T1 - Development and external validation of prediction models for critical outcomes of unvaccinated COVID-19 patients based on demographics, medical conditions and dental status
AU - Su, Naichuan
AU - Donders, Marie-Chris H. C. M.
AU - Ho, Jean-Pierre T. F.
AU - Vespasiano, Valeria
AU - de Lange, Jan
AU - Loos, Bruno G.
N1 - Funding Information: We acknowledge the dedication, commitment, and sacrifices of all personnel in our hospitals through the COVID-19 outbreak. We thank Clarinda van den Bosch-Schreuder from the Isala Academy and Jeroen Doodeman from the Northwest Academy, for their precise help with the data search. Publisher Copyright: © 2023 The Authors
PY - 2023/4/1
Y1 - 2023/4/1
N2 - Background: Multiple prediction models were developed for critical outcomes of COVID-19. However, prediction models using predictors which can be easily obtained in clinical practice and on dental status are scarce. Aim: The study aimed to develop and externally validate prediction models for critical outcomes of COVID-19 for unvaccinated adult patients in hospital settings based on demographics, medical conditions, and dental status. Methods: A total of 285 and 352 patients from two hospitals in the Netherlands were retrospectively included as derivation and validation cohorts. Demographics, medical conditions, and dental status were considered potential predictors. The critical outcomes (death and ICU admission) were considered endpoints. Logistic regression analyses were used to develop two models: for death alone and for critical outcomes. The performance and clinical values of the models were determined in both cohorts. Results: Age, number of teeth, chronic kidney disease, hypertension, diabetes, and chronic obstructive pulmonary diseases were the significant independent predictors. The models showed good to excellent calibration with observed: expected (O:E) ratios of 0.98 (95%CI: 0.76 to 1.25) and 1.00 (95%CI: 0.80 to 1.24), and discrimination with shrunken area under the curve (AUC) values of 0.85 and 0.79, based on the derivation cohort. In the validation cohort, the models showed good to excellent discrimination with AUC values of 0.85 (95%CI: 0.80 to 0.90) and 0.78 (95%CI: 0.73 to 0.83), but an overestimation in calibration with O:E ratios of 0.65 (95%CI: 0.49 to 0.85) and 0.67 (95%CI: 0.52 to 0.84). Conclusion: The performance of the models was acceptable in both derivation and validation cohorts. Number of teeth was an additive important predictor of critical outcomes of COVID-19. It is an easy-to-apply tool in hospitals for risk stratification of COVID-19 prognosis.
AB - Background: Multiple prediction models were developed for critical outcomes of COVID-19. However, prediction models using predictors which can be easily obtained in clinical practice and on dental status are scarce. Aim: The study aimed to develop and externally validate prediction models for critical outcomes of COVID-19 for unvaccinated adult patients in hospital settings based on demographics, medical conditions, and dental status. Methods: A total of 285 and 352 patients from two hospitals in the Netherlands were retrospectively included as derivation and validation cohorts. Demographics, medical conditions, and dental status were considered potential predictors. The critical outcomes (death and ICU admission) were considered endpoints. Logistic regression analyses were used to develop two models: for death alone and for critical outcomes. The performance and clinical values of the models were determined in both cohorts. Results: Age, number of teeth, chronic kidney disease, hypertension, diabetes, and chronic obstructive pulmonary diseases were the significant independent predictors. The models showed good to excellent calibration with observed: expected (O:E) ratios of 0.98 (95%CI: 0.76 to 1.25) and 1.00 (95%CI: 0.80 to 1.24), and discrimination with shrunken area under the curve (AUC) values of 0.85 and 0.79, based on the derivation cohort. In the validation cohort, the models showed good to excellent discrimination with AUC values of 0.85 (95%CI: 0.80 to 0.90) and 0.78 (95%CI: 0.73 to 0.83), but an overestimation in calibration with O:E ratios of 0.65 (95%CI: 0.49 to 0.85) and 0.67 (95%CI: 0.52 to 0.84). Conclusion: The performance of the models was acceptable in both derivation and validation cohorts. Number of teeth was an additive important predictor of critical outcomes of COVID-19. It is an easy-to-apply tool in hospitals for risk stratification of COVID-19 prognosis.
KW - COVID-19
KW - Dental status
KW - Intensive care units
KW - Medical conditions
KW - Mortality
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85152144911&partnerID=8YFLogxK
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U2 - https://doi.org/10.1016/j.heliyon.2023.e15283
DO - https://doi.org/10.1016/j.heliyon.2023.e15283
M3 - Article
C2 - 37064437
SN - 2405-8440
VL - 9
SP - 1
EP - 16
JO - Heliyon
JF - Heliyon
IS - 4
M1 - e15283
ER -
