Patient outcomes following transfer between intensive care units during the COVID-19 pandemic

Care fragmentation and readmission mortality and length of stay before and during the COVID-19 pandemic: data from the National Readmissions Database, 2018-2020

IMPORTANCE

A quarter of all 30-day readmissions involve fragmented care, where patients return to a different hospital than their original admission; these readmissions are associated with increased in-hospital mortality and longer lengths-of-stay (LOS). The stress on healthcare systems at the beginning of the COVID-19 pandemic could worsen care fragmentation and related outcomes.

OBJECTIVE

To compare fragmented readmissions in 2020 versus 2018-2019 and assess whether mortality and LOS in fragmented readmissions differed in the two time periods.

DESIGN

Observational study SETTING: National Readmissions Database (NRD), 2018-2020 PARTICIPANTS: All adults (> 18 y/o) with 30-day readmissions MAIN OUTCOMES AND MEASURES: We examined the percentage of fragmented readmissions over 2018-2020. Using unadjusted and adjusted logistic and linear regressions, we estimated the associations between fragmented readmissions and in-hospital mortality and LOS.

RESULTS

24.0-25.7% of readmissions in 2018-2020 and 27.3%-31.0% of readmissions for COVID-19 were fragmented. 2018-2019 fragmented readmissions were associated with 18-20% higher odds of in-hospital mortality compared to nonfragmented readmissions. Fragmented readmissions for COVID-19 were associated with an 18% increase in in-hospital mortality (AOR 1.18, 95% CI 1.12, 1.24). The LOS of fragmented readmissions in March-November 2018-2019 were on average 0.81 days longer, while fragmented readmissions between March-November of 2020 were associated with a 0.88-1.03 day longer LOS.

CONCLUSIONS AND RELEVANCE

A key limitation is that the NRD does not contain information on several patient/hospital-level factors that may be associated with the outcomes of interest. We observed increased fragmentation during COVID-19, but its impact on in-hospital mortality and LOS remained consistent with previous years.

Forecasting local hospital bed demand for COVID-19 using on-request simulations

Accurate forecasting of hospital bed demand is crucial during infectious disease epidemics to avoid overwhelming healthcare facilities. To address this, we developed an intuitive online tool for individual hospitals to forecast COVID-19 bed demand. The tool utilizes local data, including incidence, vaccination, and bed occupancy data, at customizable geographical resolutions. Users can specify their hospital's catchment area and adjust the initial number of COVID-19 occupied beds. We assessed the model's performance by forecasting ICU bed occupancy for several university hospitals and regions in Germany. The model achieves optimal results when the selected catchment area aligns with the hospital's local catchment. While expanding the catchment area reduces accuracy, it improves precision. However, forecasting performance diminishes during epidemic turning points. Incorporating variants of concern slightly decreases precision around turning points but does not significantly impact overall bed occupancy results. Our study highlights the significance of using local data for epidemic forecasts. Forecasts based on the hospital's specific catchment area outperform those relying on national or state-level data, striking a better balance between accuracy and precision. These hospital-specific bed demand forecasts offer valuable insights for hospital planning, such as adjusting elective surgeries to create additional bed capacity promptly.

Critical care and pandemic preparedness and response

Critical care was established partially in response to a polio epidemic in the 1950s. In the intervening 70 yr, several epidemics and pandemics have placed critical care and allied services under extreme pressure. Pandemics cause wholesale changes to accepted standards of practice, require reallocation and retargeting of resources and goals of care. In addition to clinical acumen, mounting an effective critical care response to a pandemic requires local, national, and international coordination in a diverse array of fields from research collaboration and governance to organisation of critical care networks and applied biomedical ethics in the eventuality of triage situations. This review provides an introduction to an array of topics that pertain to different states of pandemic acuity: interpandemic preparedness, alert, surge activity, recovery and relapse through the literature and experience of recent pandemics including COVID-19, H1N1, Ebola, and SARS.

Inter-hospital transfer and clinical outcomes for people with COVID-19 admitted to intensive care units in Australia: an observational cohort study

OBJECTIVES

To examine the association between inter-hospital transfer and in-hospital mortality among people with coronavirus disease 2019 (COVID-19) admitted to intensive care units (ICUs) in Australia.

DESIGN

Retrospective cohort study; analysis of data collected for the Short Period Incidence Study of Severe Acute Respiratory Illness (SPRINT-SARI) Australia study.

SETTING, PARTICIPANTS

People with COVID-19 admitted to 63 ICUs, 1 January 2020 - 1 April 2022.

MAIN OUTCOME MEASURES

Primary outcome: in-hospital mortality; secondary outcomes: ICU and hospital lengths of stay and frequency of selected complications.

RESULTS

Of 5207 people with records in the SPRINT-SARI Australia database at 1 April 2022, 328 (6.3%) had been transferred between hospitals, 305 (93%) during the third pandemic wave. Compared with patients not transferred, their median age was lower (53 years; interquartile range [IQR], 45-61 years v 60 years; IQR, 46-70 years), their median body mass index higher (32.5 [IQR, 27.2-39.0] kg/m² v 30.1 [IQR, 25.7-35.7] kg/m² ), and fewer had received a COVID-19 vaccine (22% v 44.9%); their median APACHE II scores were similar (14.0; IQR, 12.0-18.0 v 14.0; IQR, 10.0-19.0). Bacterial pneumonia (64.7% v 29.0%) and bacteraemia (27% v 8%) were more frequent in transferred patients, as was the need for more intensive ICU interventions, including invasive mechanical ventilation (71.2% v 38.1%) and extra-corporeal membrane oxygenation (26% v 1.7%). Crude ICU (19% v 14.9%) and in-hospital mortality (19% v 18.4%) were similar for patients who were or were not transferred; median lengths of ICU (20.0 [IQR, 11.2-40.3] days v 4.6 [IQR, 2.1-10.1] days) and hospital stay (29.7 [IQR, 18.1-49.6] days v 12.3 [IQR, 7.3-21.0] days) were longer for transferred patients. In the multivariable regression analysis, in-hospital mortality risk was lower for transferred patients (risk difference [RD], -5.0 percentage points; 95% confidence interval [CI] -10 to -0.03 percentage points), but not in the propensity score-adjusted analysis (RD, -3.4 [95% CI, -8.9 to 2.1] percentage points).

CONCLUSIONS

Among people with COVID-19 admitted to ICUs, patients transferred from another hospital required more intense interventions and remained in hospital longer, but were not at greater risk of dying in hospital than the patients who were not transferred.

Intensive care unit burden is associated with increased mortality in critically ill COVID-19 patients

BACKGROUND

Traditional models to predict intensive care outcomes do not perform well in COVID-19. We undertook a comprehensive study of factors affecting mortality and functional outcome after severe COVID-19.

METHODS

In this prospective multicentre cohort study, we enrolled laboratory-confirmed, critically ill COVID-19 patients at six ICUs in the Skåne Region, Sweden, between May 11, 2020, and May 10, 2021. Demographics and clinical data were collected. ICU burden was defined as the total number of ICU-treated COVID-19 patients in the region on admission. Surviving patients had a follow-up at 90 days for assessment of functional outcome using the Glasgow Outcome Scale-Extended (GOSE), an ordinal scale (1-8) with GOSE ≥5 representing a favourable outcome. The primary outcome was 90-day mortality; the secondary outcome was functional outcome at 90 days.

RESULTS

Among 498 included patients, 74% were male with a median age of 66 years and a median body mass index (BMI) of 30 kg/m² . Invasive mechanical ventilation was employed in 72%. Mortality in the ICU, in-hospital and at 90 days was 30%, 38% and 39%, respectively. Mortality increased markedly at age 60 and older. Increasing ICU burden was independently associated with a two-fold increase in mortality. Higher BMI was not associated with increased mortality. Besides age and ICU burden, smoking status, cortisone use, P_a CO₂ >7 kPa, and inflammatory markers on admission were independent factors of 90-day mortality. Lower GOSE at 90 days was associated with a longer stay in the ICU.

CONCLUSION

In critically ill COVID-19 patients, the 90-day mortality was 39% and increased considerably at age 60 or older. The ICU burden was associated with mortality, whereas a high BMI was not. A longer stay in the ICU was associated with unfavourable functional outcomes at 90 days.

Safety during interhospital helicopter transfer of ventilated COVID-19 patients. No clinical relevant changes in vital signs including non-invasive cardiac output

Background

During the COVID-19 pandemic in The Netherlands, critically ill ventilated COVID-19 patients were transferred not only between hospitals by ambulance but also by the Helicopter Emergency Medical Service (HEMS). To date, little is known about the physiological impact of helicopter transport on critically ill patients and COVID-19 patients in particular. This study was conducted to explore the impact of inter-hospital helicopter transfer on vital signs of mechanically ventilated patients with severe COVID-19, with special focus on take-off, midflight, and landing.

Methods

All ventilated critically ill COVID-19 patients who were transported between April 2020 and June 2021 by the Dutch ‘Lifeliner 5’ HEMS team and who were fully monitored, including noninvasive cardiac output, were included in this study. Three 10-min timeframes (take-off, midflight and landing) were defined for analysis. Continuous data on the vital parameters heart rate, peripheral oxygen saturation, arterial blood pressure, end-tidal CO₂ and noninvasive cardiac output using electrical cardiometry were collected and stored at 1-min intervals. Data were analyzed for differences over time within the timeframes using one-way analysis of variance. Significant differences were checked for clinical relevance.

Results

Ninety-eight patients were included in the analysis. During take-off, an increase was noticed in cardiac output (from 6.7 to 8.2 L min⁻¹; P < 0.0001), which was determined by a decrease in systemic vascular resistance (from 1071 to 739 dyne·s·cm⁻⁵, P < 0.0001) accompanied by an increase in stroke volume (from 88.8 to 113.7 mL, P < 0.0001). Other parameters were unchanged during take-off and mid-flight. During landing, cardiac output and stroke volume slightly decreased (from 8.0 to 6.8 L min⁻¹, P < 0.0001 and from 110.1 to 84.4 mL, P < 0.0001, respectively), and total systemic vascular resistance increased (P < 0.0001). Though statistically significant, the found changes were small and not clinically relevant to the medical status of the patients as judged by the attending physicians.

Conclusions

Interhospital helicopter transfer of ventilated intensive care patients with COVID-19 can be performed safely and does not result in clinically relevant changes in vital signs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02177-5.

Generalizable prediction of COVID-19 mortality on worldwide patient data

Objective

Predicting Coronavirus disease 2019 (COVID-19) mortality for patients is critical for early-stage care and intervention. Existing studies mainly built models on datasets with limited geographical range or size. In this study, we developed COVID-19 mortality prediction models on worldwide, large-scale "sparse" data and on a "dense" subset of the data.

Materials and Methods

We evaluated 6 classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), AdaBoost (AB), and Naive Bayes (NB). We also conducted temporal analysis and calibrated our models using Isotonic Regression.

Results

The results showed that AB outperformed the other classifiers for the sparse dataset, while LR provided the highest-performing results for the dense dataset (with area under the receiver operating characteristic curve, or AUC ≈ 0.7 for the sparse dataset and AUC = 0.963 for the dense one). We also identified impactful features such as symptoms, countries, age, and the date of death/discharge. All our models are well-calibrated (P > .1).

Discussion

Our results highlight the tradeoff of using sparse training data to increase generalizability versus training on denser data, which produces higher discrimination results. We found that covariates such as patient information on symptoms, countries (where the case was reported), age, and the date of discharge from the hospital or death were the most important for mortality prediction.

Conclusion

This study is a stepping-stone towards improving healthcare quality during the COVID-19 era and potentially other pandemics. Our code is publicly available at: https://doi.org/10.5281/zenodo.6336231.