Ambulances Required to Relieve Overcapacity Hospitals: A Novel Measure of Hospital Strain During the COVID-19 Pandemic in the United States

For whom the bell tolls: assessing the incremental costs associated with failure to rescue after elective colorectal surgery

BACKGROUND

Failure to rescue after elective surgery is associated with increased healthcare costs. These costs are poorly understood and have not been reported for colorectal surgery. This study aimed to assess the incremental costs of failure to rescue after elective colorectal surgery.

METHODS

This was a retrospective study of adult patients identified in the National Inpatient Sample from 2016 to 2019 who underwent an elective colectomy or proctectomy. Patients were stratified into 4 groups: uneventful recovery, successfully rescued, failure to rescue, and died without rescue attempts. "Rescue" was defined as admissions with ≥1 procedure code ≥1 day after the initial procedure. The primary outcome was total admission costs.

RESULTS

Of 451,490 admissions for elective colorectal resection, 94.6% had an uneventful recovery, 4.8% were successfully rescued, 0.4% were failure to rescue, and 0.3% died without rescue attempts. The median total hospital cost for the uneventful recovery cohort was $16,751 (IQR, $12,611-$23,116), for the successfully rescued cohort was $42,295 (IQR, $27,959-$67,077), for the failure-to-rescue cohort was $53,182 (IQR, $30,852-$95,615), and for the died without attempted rescue cohort was $29,296 (IQR, $19,812-$45,919). When comparing cost quantiles by regression analysis, failure-to-rescue patients had significantly higher costs than the successfully rescued patients for the last 3 quantiles (fifth quantile [90th percentile], $163,963 vs $106,521; P < .001).

CONCLUSION

Across a nationally representative cohort, the median total hospital costs for patients who failed to be rescued were $10,887 more than for those who were successfully rescued. These findings emphasize the importance of shared decision making and medical futility and highlight opportunities for resource optimization after postoperative complications.

Urine oxygenation predicts COVID-19 risk

Since February, 2023, the omicron variant has accounted for essentially all new coronavirus infections in Japan. If future infections involve mutant strains with the same level of infectivity and virulence as omicron, the government's basic policy will be to prevent the spread of infection, without compromising socioeconomic activities. Objectives include protecting pregnant women and elderly persons, and focusing on citizens requiring hospitalization and those at risk of serious illness, without imposing new social restrictions. Although the government tries to raise public awareness through education, most people affected by COVID-19 stay at home, and by the time patients become aware of the seriousness of their disease, it has often reached moderate or higher severity. In this review, we discuss why this situation persists even though the disease seems to have become milder with the shift from the delta variant to omicron. We also propose a pathophysiological method to determine the risk of severe illness. This assessment can be made at home in the early stages of COVID-19 infection, using urine analysis. Applicability of this method to drug discovery and development is also discussed.

Structural and social changes due to the COVID-19 pandemic and their impact on engagement in substance use disorder treatment services: a qualitative study among people with a recent history of injection drug use in Baltimore, Maryland

BACKGROUND

Substance use disorder treatment and recovery support services are critical for achieving and maintaining recovery. There are limited data on how structural and social changes due to the COVID-19 pandemic impacted individual-level experiences with substance use disorder treatment-related services among community-based samples of people who inject drugs.

METHODS

People with a recent history of injection drug use who were enrolled in the community-based AIDS Linked to the IntraVenous Experience study in Baltimore, Maryland participated in a one-time, semi-structured interview between July 2021 and February 2022 about their experiences living through the COVID-19 pandemic (n = 28). An iterative inductive coding process was used to identify themes describing how structural and social changes due to the COVID-19 pandemic affected participants' experiences with substance use disorder treatment-related services.

RESULTS

The median age of participants was 54 years (range = 24-73); 10 (36%) participants were female, 16 (57%) were non-Hispanic Black, and 8 (29%) were living with HIV. We identified several structural and social changes due the pandemic that acted as barriers and facilitators to individual-level engagement in treatment with medications for opioid use disorder (MOUD) and recovery support services (e.g., support group meetings). New take-home methadone flexibility policies temporarily facilitated engagement in MOUD treatment, but other pre-existing rigid policies and practices (e.g., zero-tolerance) were counteracting barriers. Changes in the illicit drug market were both a facilitator and barrier to MOUD treatment. Decreased availability and pandemic-related adaptations to in-person services were a barrier to recovery support services. While telehealth expansion facilitated engagement in recovery support group meetings for some participants, other participants faced digital and technological barriers. These changes in service provision also led to diminished perceived quality of both virtual and in-person recovery support group meetings. However, a facilitator of recovery support was increased accessibility of individual service providers (e.g., counselors and Sponsors).

CONCLUSIONS

Structural and social changes across several socioecological levels created new barriers and facilitators of individual-level engagement in substance use disorder treatment-related services. Multilevel interventions are needed to improve access to and engagement in high-quality substance use disorder treatment and recovery support services among people who inject drugs.

Trends in Patient Transfers From Overall and Caseload-Strained US Hospitals During the COVID-19 Pandemic

Importance

Transferring patients to other hospitals because of inpatient saturation or need for higher levels of care was often challenging during the early waves of the COVID-19 pandemic. Understanding how transfer patterns evolved over time and amid hospital overcrowding could inform future care delivery and load balancing efforts.

Objective

To evaluate trends in outgoing transfers at overall and caseload-strained hospitals during the COVID-19 pandemic vs prepandemic times.

Design, Setting, and Participants

This retrospective cohort study used data for adult patients at continuously reporting US hospitals in the PINC-AI Healthcare Database. Data analysis was performed from February to July 2023.

Exposures

Pandemic wave, defined as wave 1 (March 1, 2020, to May 31, 2020), wave 2 (June 1, 2020, to September 30, 2020), wave 3 (October 1, 2020, to June 19, 2021), Delta (June 20, 2021, to December 18, 2021), and Omicron (December 19, 2021, to February 28, 2022).

Main Outcomes and Measures

Weekly trends in cumulative mean daily acute care transfers from all hospitals were assessed by COVID-19 status, hospital urbanicity, and census index (calculated as daily inpatient census divided by nominal bed capacity). At each hospital, the mean difference in transfer counts was calculated using pairwise comparisons of pandemic (vs prepandemic) weeks in the same census index decile and averaged across decile hospitals in each wave. For top decile (ie, high-surge) hospitals, fold changes (and 95% CI) in transfers were adjusted for hospital-level factors and seasonality.

Results

At 681 hospitals (205 rural [30.1%] and 476 urban [69.9%]; 360 [52.9%] small with <200 beds and 321 [47.1%] large with ≥200 beds), the mean (SD) weekly outgoing transfers per hospital remained lower than the prepandemic mean of 12.1 (10.4) transfers per week for most of the pandemic, ranging from 8.5 (8.3) transfers per week during wave 1 to 11.9 (10.7) transfers per week during the Delta wave. Despite more COVID-19 transfers, overall transfers at study hospitals cumulatively decreased during each high national surge period. At 99 high-surge hospitals, compared with a prepandemic baseline, outgoing acute care transfers decreased in wave 1 (fold change -15.0%; 95% CI, -22.3% to -7.0%; P < .001), returned to baseline during wave 2 (2.2%; 95% CI, -4.3% to 9.2%; P = .52), and displayed a sustained increase in subsequent waves: 19.8% (95% CI, 14.3% to 25.4%; P < .001) in wave 3, 19.2% (95% CI, 13.4% to 25.4%; P < .001) in the Delta wave, and 15.4% (95% CI, 7.8% to 23.5%; P < .001) in the Omicron wave. Observed increases were predominantly limited to small urban hospitals, where transfers peaked (48.0%; 95% CI, 36.3% to 60.8%; P < .001) in wave 3, whereas large urban and small rural hospitals displayed little to no increases in transfers from baseline throughout the pandemic.

Conclusions and Relevance

Throughout the COVID-19 pandemic, study hospitals reported paradoxical decreases in overall patient transfers during each high-surge period. Caseload-strained rural (vs urban) hospitals with fewer than 200 beds were unable to proportionally increase transfers. Prevailing vulnerabilities in flexing transfer capabilities for care or capacity reasons warrant urgent attention.

Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province

OBJECTIVE

To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model.

METHODS

The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared.

RESULTS

The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect.

CONCLUSION

Both models can better fit the number of admissions, and SARIMA model has better prediction effect.

Decreasing length of stay in breast reconstruction patients: A national analysis of 2019-2020

BACKGROUND

The effects of COVID-19 on breast reconstruction included shifts toward alloplastic reconstruction methods to preserve hospital resources and minimize COVID exposures. We examined the effects of COVID-19 on breast reconstruction hospital length of stay (LOS) and subsequent early postoperative complication rates.

METHODS

Using the National Surgical Quality Improvement Program, we examined female patients who underwent mastectomy with immediate breast reconstruction from 2019 to 2020. We compared postoperative complications across 2019-2020 for alloplastic and autologous reconstruction patients. We further performed subanalysis of 2020 patients based on LOS.

RESULTS

Both alloplastic and autologous reconstruction patients had shorter inpatient stays. Regarding the alloplastic 2019 versus 2020 cohorts, complication rates did not differ (p > 0.05 in all cases). Alloplastic patients in 2020 with longer LOS had more unplanned reoperations (p < 0.001). Regarding autologous patients in 2019 versus 2020, the only complication increasing from 2019 to 2020 was deep surgical site infection (SSI) (2.0% vs. 3.6%, p = 0.024). Autologous patients in 2020 with longer LOS had more unplanned reoperations (p = 0.007).

CONCLUSIONS

In 2020, hospital LOS decreased for all breast reconstruction patients with no complication differences in alloplastic patients and a slight increase in SSIs in autologous patients. Shorter LOS may lead to improved satisfaction and lower healthcare costs with low complication risk, and future research should examine the potential relationship between LOS and these outcomes.