A regionally tailored epidemiological forecast and monitoring program to guide a healthcare system in the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, analytics and predictive models built on regional data provided timely, accurate monitoring of epidemiological behavior, informing critical planning and decision-making for health system leaders. At Atrium Health, a large, integrated healthcare system in the southeastern United States, a team of statisticians and physicians created a comprehensive forecast and monitoring program that leveraged an array of statistical methods. METHODS: The program utilized the following methodological approaches: (i) exploratory graphics, including time plots of epidemiological metrics with smoothers; (ii) infection prevalence forecasting using a Bayesian epidemiological model with time-varying infection rate; (iii) doubling and halving times computed using changepoints in local linear trend; (iv) death monitoring using combination forecasting with an ensemble of models; (v) effective reproduction number estimation with a Bayesian approach; (vi) COVID-19 patients hospital census monitored via time series models; and (vii) quantified forecast performance. RESULTS: A consolidated forecast and monitoring report was produced weekly and proved to be an effective, vital source of information and guidance as the healthcare system navigated the inherent uncertainty of the pandemic. Forecasts provided accurate and precise information that informed critical decisions on resource planning, bed capacity and staffing management, and infection prevention strategies. CONCLUSIONS: In this paper, we have presented the framework used in our epidemiological forecast and monitoring program at Atrium Health, as well as provided recommendations for implementation by other healthcare systems and institutions to facilitate use in future pandemics.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.

Modeling COVID-19 Latent Prevalence to Assess a Public Health Intervention at a State and Regional Scale: Retrospective Cohort Study.

BACKGROUND: Emergence of the coronavirus disease (COVID-19) caught the world off guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their health care systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policy makers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina and the Charlotte Metropolitan Region, and to incorporate the effect of a public health intervention to reduce disease spread while accounting for unique regional features and imperfect detection. METHODS: Three SIR models were fit to infection prevalence data from North Carolina and the greater Charlotte Region and then rigorously compared. One of these models (SIR-int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics such as the basic reproduction number. RESULTS: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte Region doubling time of 2.56 days (95% CI 2.11-3.25) and in North Carolina 2.94 days (95% CI 2.33-4.00). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte Region to 4.70 days (95% CI 3.77-6.22) and in North Carolina to 4.01 days (95% CI 3.43-4.83). Reflecting spatial differences, this deceleration favored the greater Charlotte Region compared to North Carolina as a whole. A comparison of the efficacy of intervention, defined as 1 - the hazard ratio of infection, gave 0.25 for North Carolina and 0.43 for the Charlotte Region. In addition, early in the pandemic, the initial basic SIR model had good fit to the data; however, as the pandemic and local conditions evolved, the SIR-int model emerged as the model with better fit. CONCLUSIONS: Using local data and continuous attention to model adaptation, our findings have enabled policy makers, public health officials, and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.

The Effect of Outcome Selection on the Performance of Prediction Models in Patients at Risk for Sepsis.

IMPORTANCE: Risk prediction models for patients with suspected sepsis have been derived on and applied to various outcomes, including readily available outcomes such as hospital mortality and ICU admission as well as longer-term mortality outcomes that may be more important to patients. It is unknown how selecting different outcomes influences model performance in patients at risk for sepsis. OBJECTIVES: Evaluate the impact of outcome selection on risk model performance and weighting of individual predictor variables. DESIGN SETTING AND PARTICIPANTS: We retrospectively analyzed adults hospitalized with suspected infection from January 2014 to September 2017 at 12 hospitals. MAIN OUTCOMES AND MEASURES: We used routinely collected clinical data to derive logistic regression models for four outcomes: hospital mortality, composite ICU length of stay greater than 72 hours or hospital mortality, 30-day mortality, and 90-day mortality. We compared the performance of the models using area under the receiver operating characteristic curve and calibration plots. RESULTS: Among 52,184 admissions, 2,030 (4%) experienced hospital mortality, 6,659 (13%) experienced the composite of hospital mortality or ICU length of stay greater than 72 hours, 3,417 (7%) experienced 30-day mortality, and 5,655 (11%) experienced 90-day mortality. Area under the receiver operating characteristic curves decreased when hospital-based models were applied to predict 30-day (hospital mortality = 0.88-0.85; -0.03, composite ICU length of stay greater than 72 hours or hospital mortality = 0.90-0.81; -0.09) and 90-day mortality (hospital mortality = 0.88-0.81; -0.07, composite ICU length of stay greater than 72 hours or hospital mortality = 0.90-0.76; -0.14; all p < 0.01). Models were well calibrated for derived (root-mean-square error = 5-15) but not alternate outcomes (root-mean-square error = 8-35). CONCLUSIONS AND RELEVANCE: Risk models trained to predict readily available hospital-based outcomes in suspected sepsis show poorer discrimination and calibration when applied to 30- and 90-day mortality. Interpretation and application of risk models for patients at risk of sepsis should consider these findings.

Association between Adherence to Recommended Care and Outcomes for Adult Survivors of Sepsis.

Rationale: Postsepsis care recommendations target specific deficits experienced by sepsis survivors in elements such as optimization of medications, screening for functional impairments, monitoring for common and preventable causes of health deterioration, and consideration of palliative care. However, few data are available regarding the application of these elements in clinical practice.Objectives: To quantify the delivery of postsepsis care for patients discharged after hospital admission for sepsis and evaluate the association between receipt of postsepsis care elements and reduced mortality and hospital readmission within 90 days.Methods: We conducted a retrospective chart review of a random sample of patients who were discharged alive after an admission for sepsis (identified from International Classification of Diseases, 10th Revision discharge codes) at 10 hospitals during 2017. We used a structured chart abstraction to determine whether four elements of postsepsis care were provided within 90 days of hospital discharge, per expert recommendations. We used multivariable logistic regression to evaluate the association between receipt of care elements and 90-day hospital readmission and mortality, adjusted for age, comorbidity, length of stay, and discharge disposition.Results: Among 189 sepsis survivors, 117 (62%) had medications optimized, 123 (65%) had screening for functional or mental health impairments, 86 (46%) were monitored for common and preventable causes of health deterioration, and 110 (58%) had care alignment processes documented (i.e., assessed for palliative care or goals of care). Only 20 (11%) received all four care elements within 90 days. Within 90 days of discharge, 66 (35%) patients were readmitted and 33 (17%) died (total patients readmitted or died, n = 82). Receipt of two (odds ratio [OR], 0.26; 95% confidence interval [95% CI], 0.10-0.69) or more (three OR, 0.28; 95% CI, 0.11-0.72; four OR, 0.12; 95% CI, 0.03-0.50) care elements was associated with lower odds of 90-day readmission or 90-day mortality compared with zero or one element documented. Optimization of medications (no medication errors vs. one or more errors; OR, 0.44; 95% CI, 0.21-0.92), documented functional or mental health assessments (physical function plus swallowing/mental health assessments vs. no assessments; OR, 0.14; 95% CI, 0.05-0.40), and documented goals of care or palliative care screening (OR, 0.52; 95% CI, 0.25-1.05; not statistically significant) were associated with lower odds of 90-day readmission or 90-day mortality.Conclusions: In this retrospective cohort study of data from a single health system, we found variable delivery of recommended postsepsis care elements that were associated with reduced morbidity and mortality after hospitalization for sepsis. Implementation strategies to efficiently overcome barriers to adopting recommended postsepsis care may help improve outcomes for sepsis survivors.

Early clinical experience with collagen nerve tubes in digital nerve repair.

PURPOSE: In cases of digital nerve injury in which nerve ends cannot be approximated without tension, autologous nerve grafts represent the most commonly used method for reconstruction. Recently, interest in synthetic nerve guides as an alternative to grafting has increased. Although several basic science studies have shown promise for collagen tubes, clinical studies of their success in humans are limited. The purpose of this study was to review our early clinical experience with collagen nerve tubes. METHODS: The authors identified and followed all cases involving digital nerve repair at our institution over a 2-year period. Twelve patients had repair of a digital nerve with a collagen nerve tube during the study period. Two patients were lost to follow-up, and 1 patient had amputation of the grafted finger secondary to complications of other injuries. The primary outcome data points for the remaining 9 patients were the static 2-point discrimination (2PD), Semmes-Weinstein monofilament testing, and a Quick Disabilities of the Arm, Shoulder, and Hand (DASH) outcome survey at final follow-up. RESULTS: Nine patients had follow-up of at least 1 year, with an average follow-up time of 15 months (range 12-22 months). There were no intraoperative or postoperative complications related to the nerve tubes. Using modified American Society for Surgery of the Hand guidelines, 2PD results were good or excellent in 8 out of 9 of patients. Semmes-Weinstein testing results were full in 5 patients, diminished light touch in 2, diminished protective sensation in 1, and loss of protective sensation in 1. Average Quick DASH scores for the group were 10.86 overall, 4.86 for the work module, and 23.21 for the sports/performing arts module. CONCLUSIONS: Although the patients in this study are still within the early follow-up period, our initial results compare favorably with those reported in the existing literature for various types of nerve repair and reconstruction, suggesting that collagen nerve tubes might offer a clinically effective option for restoration of sensory function. TYPE OF STUDY/LEVEL OF EVIDENCE: Therapeutic IV.

Complications in dorsal percutaneous cannulated screw fixation of nondisplaced scaphoid waist fractures.

PURPOSE: With advances in tools and techniques, percutaneous screw fixation of nondisplaced fractures of the scaphoid waist has gained increasing popularity in recent years as an alternative to prolonged cast immobilization or open reduction and internal fixation. Many reports cite low complication rates, including no complications in some series. The purpose of this study was to evaluate the complications encountered with dorsal percutaneous cannulated screw fixation of nondisplaced scaphoid waist fractures. METHODS: A retrospective chart review was performed for 24 patients who had surgery performed by a single surgeon over a 5-year period. All cases involved dorsal percutaneous cannulated screw fixation of nondisplaced (<1 mm) fractures of the scaphoid waist. Complications were rated a priori as major or minor based on modifications of established criteria. RESULTS: The overall complication rate was 29%; there were 21% (5/24) major complications and 8% (2/24) minor complications. Major complications consisted of 1 case of nonunion, 3 cases involving hardware problems, and 1 case of postoperative fracture of the proximal pole of the scaphoid. Minor complications included intraoperative equipment breakage-1 case involving a screw and 1 case involving a guide wire. CONCLUSIONS: Complications in dorsal percutaneous cannulated screw fixation of scaphoid fractures may be more common than previously reported.