Background:The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.Objective: To estimate the timing of surges in clinical demand and the best-and worst-case scenarios of local COVID-19induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.Design: Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.Setting: 3 hospitals in an academic health system. Patients:All people living in the greater Philadelphia region. Measurements:The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.Results: Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best-and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.Limitations: Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction. Conclusion:Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

Key PointsQuestionIs adding preoperative and intraoperative data associated with improved risk stratification of patients undergoing noncardiac surgery for postoperative acute kidney injury?FindingsIn this prognostic study of 42 615 patients who underwent noncardiac surgery, the addition of preoperative to prehospitalization data improved model performance (area under the curve increased from 0.71 to 0.80) as did adding preoperative plus intraoperative data (area under the curve further increased to 0.82).MeaningAlthough electronic health record data may be used to accurately stratify patients at risk of postoperative acute kidney injury, there appears to be only modest improvement in performance when adding intraoperative data to risk stratification models.

BACKGROUND/OBJECTIVE: Risk-stratification tools for cardiac complications after noncardiac surgery based on preoperative risk factors are used to inform postoperative management. However, there is limited evidence on whether risk stratification can be improved by incorporating data collected intraoperatively, particularly for low-risk patients. METHODS: We conducted a retrospective cohort study of adults who underwent noncardiac surgery between 2014 and 2018 at four hospitals in the United States. Logistic regression with elastic net selection was used to classify in-hospital major adverse cardiovascular events (MACE) using preoperative and intraoperative data (“perioperative model”). We compared model performance to standard risk stratification tools and professional society guidelines that do not use intraoperative data. RESULTS: Of 72,909 patients, 558 (0.77%) experienced MACE. Those with MACE were older and less likely to be female. The perioperative model demonstrated an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.85-0.92). This was higher than the Lee Revised Cardiac Risk Index (RCRI) AUC of 0.79 (95% CI, 0.74-0.84; P < .001 for AUC comparison). There were more MACE complications in the top decile (n = 1,465) of the perioperative model’s predicted risk compared with that of the RCRI model (n = 58 vs 43). Additionally, the perioperative model identified 2,341 of 7,597 (31%) patients as low risk who did not experience MACE but were recommended to receive postoperative biomarker testing by a risk factor–based guideline algorithm. CONCLUSIONS: Addition of intraoperative data to preoperative data improved prediction of cardiovascular complication outcomes after noncardiac surgery and could potentially help reduce unnecessary postoperative testing.