COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

ObjectivesDevelop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19.DesignRetrospective.SettingSecondary care in four large Dutch hospitals.ParticipantsPatients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation.Outcome measuresWe developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots.ResultsOf 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model—COVID outcome prediction in the emergency department (COPE)—with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86);0.82 (95% CI 0.74 to 0.90);0.79 (95% CI 0.70 to 0.88);0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90);0.81 (95% CI 0.66 to 0.95)).ConclusionsCOPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

Changes in the quality of cancer care as assessed through performance indicators during the first wave of the COVID-19 pandemic in 2020: a Scoping Review (preprint)

Objectives: Summarize performance indicators used in the literature to evaluate the impact of the COVID-19 pandemic on cancer care (January-June 2020), and to assess changes in the quality of care as assessed via selected indicators. Methods: Scoping review. Indicators and their reported trends were collated following the cancer care pathway. Results: Database searches retrieved 6277 articles, 838 articles met the inclusion criteria, and 135 articles were included after full-text screening, from which 917 indicators were retrieved. Indicators assessing the diagnostic process showed a decreasing trend: from 33 indicators reporting on screening, 30 (91%) signalled a decrease during the pandemic (n=30 indicators, 91%). A reduction was also observed in the number of diagnostic procedures (n=64, 58%) and in the diagnoses (n=130, 89%). The proportion of diagnoses in the emergency setting and waiting times showed an increasing trend (n=8, 89% and n=14, 56%, respectively). Nine indicators (64%) showed stability in cancer stages distribution. A decreasing trend in the proportion of earliest stage cancers was reported by 63% of indicators (n=9), and 70% (n=43) of indicators showed an increasing trend in the proportion of advanced-stage cancers. Indicators reflecting the treatment process signalled a reduction in the number of procedures: 79% (n=82) of indicators concerning surgeries, 72% (n=41) of indicators assessing trends in radiotherapy, and 93% (n=40) of indicators related to systemic therapies. Modifications in cancer treatment were frequently reported: 64% (n=195) of indicators revealed changes in treatment. Ten indicators (83%) signalled a decreasing number of hospital admissions. Conclusion: Health systems struggled to ensure continuity of cancer care. As this pandemic keeps evolving, the trends reported over the first 6 months of 2020 provide an argument to monitor these changes closely. This information needs to be transparent, standardised, and timely, allowing to monitor quality and outcomes of care during crises and inform policy responses.

The impact of the COVID-19 pandemic on hospital services for patients with cardiac diseases: a scoping review (preprint)

Aim: To assess the impact of the COVID-19 pandemic on hospital care for cardiac patients. Methods and results: Scoping review, including studies with empirical data on changes in the use of health services measured by performance indicators during January - June 2020. Database searches yielded 6277 articles, of which 838 articles met the inclusion criteria during initial screening. After full-text screening, 94 articles were considered for data extraction. In total, 1637 indicators were retrieved, showing large variation in the indicators and their definitions. Most of the indicators that provided information on changes in number of admissions (n=118, 88%) signalled a decrease in admissions; 88% (n=15) of the indicators showed patients delayed presentation and 40% (n=54) showed patients in a worse clinical condition. A reduction in diagnostic and treatment procedures was signalled by 95% (n=18) and 81% (n=64) of the indicators reporting on cardiac procedures, respectively. Length of stay decreased in 58% (n=21) of the indicators and acute coronary syndromes treatment times increased in 61% (n=65) of the indicators. Outpatient activity decreased in 94% (n=17) of the indicators related with outpatient care, whereas telehealth utilization increased in 100% (n=6). Outcomes worsened in 40% (n=35) of the indicators, and mortality rates increased in 52% (n=31). Conclusion: All phases of the hospital cardiac care pathway were affected. This information could support the planning of care during the ongoing pandemic and in future events. Furthermore, to ensure continuity of care during crises, fostering the use of standardised indicators is paramount.

COVID Outcome Prediction in the Emergency Department (COPE) (preprint)

Background and aimThe COVID-19 pandemic is putting extraordinary pressure on emergency departments (EDs). To support decision making about hospital admission, we aimed to develop a simple and valid model for predicting mortality and need for admission to an intensive care unit (ICU) in suspected-COVID-19 patients presenting at the ED. MethodsFor model development, we included patients that presented at the ED and were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020, the first wave of the pandemic in the Netherlands. Based on prior literature we included patient characteristics, vital parameters and blood test values, all measured at ED admission, as potential predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of being admitted to the ICU, both within 28 days after admission. Model performance (AUC; calibration plots, intercepts and slopes) was assessed with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing predictor values. ResultsThe development data included 5,831 patients who presented at the ED and were hospitalized, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. A simple model - named COVID Outcome Prediction in the Emergency Department (COPE) - with linear age and logarithmic transforms of respiratory rate, CRP, LDH, albumin and urea captured most of the ability to predict death within 28 days. Patients who were admitted in the first month of the pandemic had substantially increased risk of death (odds ratio 1.99; 95% CI 1.61-2.47). COPE was well-calibrated and showed good discrimination for predicting death in 3,252 patients of the second wave (AUC in 4 hospitals: 0.82; 0.82; 0.79; 0.83). COPE was also able to identify patients at high risk of needing IC in second wave patients below the age of 70 (AUC 0.84; 0.81), but overestimated ICU admission for low-risk patients. The models are implemented as a web-based application. ConclusionCOPE is a simple tool that is well able to predict mortality and ICU admission for patients who present to the ED with suspected COVID-19 and may help to inform patients and doctors when deciding on hospital admission.

Minimizing Population Health Loss in Times of Scarce Surgical Capacity (preprint)

Background COVID-19 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model that supports prioritization of care from a utilitarian perspective, which is to minimize population health loss. Methods A cohort state-transition model was developed and applied to 43 semi-elective non-paediatric surgeries commonly performed in academic hospitals. Scenarios of delaying surgery from two weeks were compared with delaying up to one year, and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization global burden of disease study. For each surgery, the urgency was estimated as the average expected loss of Quality-Adjusted Life-Years (QALYs) per month. Results Given the best available evidence, the two most urgent surgeries were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 QALY loss/month, 95%-CI: 0.09-0.24) and transaortic valve implantation (0.15 QALY loss/month, 95%-CI: 0.09-0.24). The two least urgent surgeries were placing a shunt for dialysis (0.01, 95%-CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95%-CI: 0.01-0.02): these surgeries were associated with a limited amount of health lost on the waiting list. Conclusion Expected health loss due to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgeries to minimize population health loss in times of scarcity. This tool should yet be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.