摘要Objective: To construct a risk prediction model for in-hospital cardiac arrest (IHCA) in patients with chronic obstructive pulmonary disease(COPD) complicated with respiratory failure. Methods: The clinical data of 550 patients with COPD complicated with respiratory failure in our hospital from January 2016 to June 2022 were retrospectively analyzed. According to the occurrence of IHCA, they were divided into the IHCA group and non-IHCA group. The general data and clinical indicators of the two groups were compared, and logistic regression analysis was performed, R software was used to establish the risk prediction model (nomogram model) for predicting the occurrence of IHCA in patients with COPD complicated with respiratory failure. The risk prediction model (line graph model) for patients with IHCA was validated by the Bootstrap method, and the predictive value was analyzed by applying the receiver operating characteristic(ROC) curve. Results: Among 550 COPD patients complicated with respiratory failure, 95 cases (17.27%) had IHCA. There were significant differences in age, old myocardial infarction, heart failure, moderate and severe chronic kidney disease, assisted breathing mode, state of consciousness, body temperature, heart rate, respiratory rate, systolic blood pressure, lactic acid, SaO2, PaCO2, serum creatinine, albumin, and prealbumin between the non-IHCA group and IHCA group (P<0.05). Logistic regression analysis showed that age, heart failure, heart rate, respiratory rate, systolic blood pressure, unclear state of consciousness, serum creatinine and prealbumin were independent influencing factors of IHCA in COPD patients complicated with respiratory failure(P<0.05). According to the results of binary logistic regression analysis, a nomogram model for predicting the incidence of IHCA in COPD patients complicated with respiratory failure was constructed. The fitting degree of the model was determined by the H-L test. The calibration curve showed that the incidence of IHCA in COPD patients complicated with respiratory failure predicted by nomogram was in good agreement with the actual incidence of IHCA in patients with COPD complicated with respiratory failure（χ2=2.017, P=0.334). The ROC curve showed an AUC of 0.627 (95% CI: 0.593-0.689, P<0.005), and the optimal cut point value for diagnosis was 0.69, at which the sensitivity and specificity were 42.57% and 96.03%, respectively. Conclusion: According to the independent influencing factors of IHCA in COPD patients complicated with respiratory failure, the establishment of a risk prediction nomogram model has high predictive value, which is worthy of clinical promotion.
Abstract：Objective: To construct a risk prediction model for in-hospital cardiac arrest (IHCA) in patients with chronic obstructive pulmonary disease(COPD) complicated with respiratory failure. Methods: The clinical data of 550 patients with COPD complicated with respiratory failure in our hospital from January 2016 to June 2022 were retrospectively analyzed. According to the occurrence of IHCA, they were divided into the IHCA group and non-IHCA group. The general data and clinical indicators of the two groups were compared, and logistic regression analysis was performed, R software was used to establish the risk prediction model (nomogram model) for predicting the occurrence of IHCA in patients with COPD complicated with respiratory failure. The risk prediction model (line graph model) for patients with IHCA was validated by the Bootstrap method, and the predictive value was analyzed by applying the receiver operating characteristic(ROC) curve. Results: Among 550 COPD patients complicated with respiratory failure, 95 cases (17.27%) had IHCA. There were significant differences in age, old myocardial infarction, heart failure, moderate and severe chronic kidney disease, assisted breathing mode, state of consciousness, body temperature, heart rate, respiratory rate, systolic blood pressure, lactic acid, SaO2, PaCO2, serum creatinine, albumin, and prealbumin between the non-IHCA group and IHCA group (P<0.05). Logistic regression analysis showed that age, heart failure, heart rate, respiratory rate, systolic blood pressure, unclear state of consciousness, serum creatinine and prealbumin were independent influencing factors of IHCA in COPD patients complicated with respiratory failure(P<0.05). According to the results of binary logistic regression analysis, a nomogram model for predicting the incidence of IHCA in COPD patients complicated with respiratory failure was constructed. The fitting degree of the model was determined by the H-L test. The calibration curve showed that the incidence of IHCA in COPD patients complicated with respiratory failure predicted by nomogram was in good agreement with the actual incidence of IHCA in patients with COPD complicated with respiratory failure（χ2=2.017, P=0.334). The ROC curve showed an AUC of 0.627 (95% CI: 0.593-0.689, P<0.005), and the optimal cut point value for diagnosis was 0.69, at which the sensitivity and specificity were 42.57% and 96.03%, respectively. Conclusion: According to the independent influencing factors of IHCA in COPD patients complicated with respiratory failure, the establishment of a risk prediction nomogram model has high predictive value, which is worthy of clinical promotion.
LI Sai-yu, LIN Zhao-sheng, LI You-tang, WENG Duan-li. Construction of Risk Prediction Model of Cardiac Arrest in Patients with Chronic Obstructive Pulmonary Disease Complicated with Respiratory Failure[J]. 中国生物医学工程学报(英文版）, 2022, 31(3): 101-111.
LI Sai-yu, LIN Zhao-sheng, LI You-tang, WENG Duan-li. Construction of Risk Prediction Model of Cardiac Arrest in Patients with Chronic Obstructive Pulmonary Disease Complicated with Respiratory Failure. Chinese Journal of Biomedical Engineering, 2022, 31(3): 101-111.
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