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1.
BMJ Open ; 12(6): e060354, 2022 06 22.
Article in English | MEDLINE | ID: covidwho-1902014

ABSTRACT

INTRODUCTION: During the COVID-19 pandemic many non-acute elective surgeries were cancelled or postponed around the world. This has created an opportunity to study the effect of delayed surgery on health conditions prior to surgery and postsurgical outcomes in patients with postponed elective surgery. The control group of the Routine Postsurgical Anesthesia Visit to Improve Patient Outcome (TRACE I) study, conducted between 2016 and 2019, will serve as a control cohort. METHODS AND ANALYSIS: TRACE II is an observational, multicentre, prospective cohort study among surgical patients with postponed surgery due to COVID-19 in academic and non-academic hospitals in the Netherlands. We aim to include 2500 adult patients. The primary outcome will be the 30-day incidence of major postoperative complications. Secondary outcome measures include the 30-day incidence of minor postoperative complications, 1 year mortality, length of stay (in hospital, medium care and intensive care), quality of recovery 30 days after surgery and postoperative quality of life up to 1 year following surgery. Multivariable logistic mixed-effects regression analysis with a random intercept for hospital will be used to test group differences on the primary outcome. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Institutional Review Board of Maastricht University Medical Centre+ and Amsterdam UMC. Findings will be presented at national and international conferences, as well as published in peer-reviewed scientific journals, with a preference for open access journals. Data will be made publicly available after publication of the main results. TRIAL REGISTRATION NUMBER: NL8841.


Subject(s)
COVID-19 , Adult , COVID-19/epidemiology , Humans , Multicenter Studies as Topic , Observational Studies as Topic , Pandemics , Postoperative Complications/epidemiology , Prospective Studies , Quality of Life
2.
BMJ Open ; 11(7): e047347, 2021 07 19.
Article in English | MEDLINE | ID: covidwho-1318029

ABSTRACT

OBJECTIVE: Develop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital. DESIGN: Retrospective cohort study. SETTING: A multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020. PARTICIPANTS: SARS-CoV-2 positive patients (age ≥18) admitted to the hospital. MAIN OUTCOME MEASURES: 21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis. RESULTS: 2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81). CONCLUSION: Both models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.


Subject(s)
COVID-19 , Cohort Studies , Humans , Logistic Models , Retrospective Studies , SARS-CoV-2
3.
PLoS One ; 16(4): e0249920, 2021.
Article in English | MEDLINE | ID: covidwho-1186609

ABSTRACT

OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.


Subject(s)
COVID-19/mortality , Age Factors , Aged , Aged, 80 and over , Belgium/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Cohort Studies , Communicable Disease Control , Comorbidity , Electronic Health Records , Female , Hospitalization , Humans , Male , Middle Aged , Netherlands/epidemiology , Prognosis , Risk Assessment , Risk Factors , SARS-CoV-2/isolation & purification
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