Time spent in prior hospital stay and outcomes for ventilator patients in long-term acute care hospitals

Association between preoperative hemoglobin with length of hospital stay among non-cardiac and non-obstetric surgery patients: a secondary analysis of a retrospective cohort study

BACKGROUND

Previous studies concerning the association between preoperative Hemoglobin (HB) level and the Length Of hospital Stay (LOS) in patients with non-cardiac surgery and non-obstetric surgery remain inconclusive. Herein, the objective of this study was to analyze whether and to what extent the preoperative HB level was connected with the LOS in non-cardiac and non-obstetric surgery patients.

METHODS

This retrospective cohort study was performed at a single institution, involving patients who underwent elective non-cardiac, non-obstetric surgery from April 2007 to September 2013. Clinical characteristics of patients such as demographics, comorbidities, preoperative HB level, LOS, mortality, procedure length, and pulmonary hypertension (PHTN) Severity Class data were collected. A univariate analysis was used to determine the association between clinical characteristics and LOS. Multivariate regression analysis was conducted to investigate the relationship between preoperative HB level and LOS.

RESULTS AND DISCUSSION

In this study, 311 patients were included. We observed that compared with the LOS > 7 days group, the average HB level of patients in the LOS ≤ 7 days group was higher (12.04 ± 2.20 g/dl vs. 10.92 ± 2.22 g/dl, p < 0.001). In addition, there were fewer patients with moderate-to-severe anemia in LOS ≤ 7 days group than the LOS > 7 days group (32.74% vs 58.82%, p < 0.001). In addition, we found that patients with LOS ≤ 7 days were accompanied with lower mortality (0.44% vs. 7.06%, p < 0.001) and lower mean combined pulmonary artery systolic pressure (PASP) and right ventricular systolic pressure (RVSP) than that in patients with LOS > 7 days (42.56 ± 11.97 vs. 46.00 ± 12.37, p < 0.05). After controlling for relevant confounders, we discovered a nonlinear association between preoperative HB level and LOS as well as a threshold effect based on LOS. Specifically, when preoperative HB level was less than 11.9 g/dL, LOS decreased by 2 days for each 1 g/dL increase in HB level. However, LOS did not alter substantially with the rise of preoperative HB level when it was higher than 11.9 g/dL.

CONCLUSION

Our study showed a close non-linear association between preoperative HB level and LOS in patients with non-cardiac surgery and non-obstetric surgery. In particular, for patients with preoperative HB less than 11.9 g/dL, increasing the preoperative HB level can help shorten the LOS after operation.

Characteristics and Outcomes of Tracheostomized Patients With and Without COVID-19

Outcomes of tracheostomized patients with COVID-19 are seldomly investigated with conflicting evidence from the existing literature.

OBJECTIVES

To create a study evaluating the impact of COVID-19 on tracheostomized patients by comparing clinical outcomes and weaning parameters in COVID-19 positive and negative cohorts.

DESIGN SETTING AND PARTICIPANTS

A retrospective observational cohort study of 604 tracheostomized patients hospitalized in 16 ICUs in New York City between March 9, 2020, and September 8, 2021.

MAIN OUTCOMES AND MEASURES

Patients were stratified into two cohorts: 398 COVID-19 negative (COVID-ve) and 206 COVID-19 positive (COVID+ve) patients. Clinical characteristics, outcomes, and weaning parameters (first pressure support [PS], tracheostomy collar [TC], speech valve placement, and decannulation) were analyzed.

RESULTS

COVID+ve had fewer comorbidities including coronary artery disease, congestive heart failure, malignancy, chronic kidney disease, liver disease, and HIV (p < 0.05). Higher Fio2 (53% vs 44%), positive end-expiratory pressure (PEEP) (7.15 vs 5.69), Pco2 (45.8 vs 38.2), and lower pH (7.41 vs 7.43) were observed at the time of tracheostomy in COVID+ve (p < 0.005). There was no statistical difference in post-tracheostomy complication rates. Longer time from intubation to tracheostomy (15.90 vs 13.60 d; p = 0.002), tracheostomy to first PS (2.87 vs 1.80 d; p = 0.005), and TC placement (11.07 vs 4.46 d; p < 0.001) were seen in COVID+ve. However, similar time to speech valve placement, decannulation, and significantly lower 1-year mortality (23.3% vs 36.7%; p = 0.001) with higher number of discharges to long-term acute care hospital (LTACH) (23.8% vs 13.6%; p = 0.015) were seen in COVID+ve.

CONCLUSIONS AND RELEVANCE

Patients with COVID-19 required higher Fio2 and PEEP ventilatory support at the time of tracheostomy, with no observed change in complication rates. Despite longer initial weaning period with PS or TC, similar time to speech valve placement or decannulation with significantly lower mortality and higher LTACH discharges suggest favorable outcome in COVID-19 positive patients. Higher ventilatory support requirements and prolonged weaning should not be a deterrent to pursuing a tracheostomy.

Analysis of the Cardiorespiratory Pattern of Patients Undergoing Weaning Using Artificial Intelligence

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

Biosignal-Based Digital Biomarkers for Prediction of Ventilator Weaning Success

We evaluated new features from biosignals comprising diverse physiological response information to predict the outcome of weaning from mechanical ventilation (MV). We enrolled 89 patients who were candidates for weaning from MV in the intensive care unit and collected continuous biosignal data: electrocardiogram (ECG), respiratory impedance, photoplethysmogram (PPG), arterial blood pressure, and ventilator parameters during a spontaneous breathing trial (SBT). We compared the collected biosignal data's variability between patients who successfully discontinued MV (n = 67) and patients who did not (n = 22). To evaluate the usefulness of the identified factors for predicting weaning success, we developed a machine learning model and evaluated its performance by bootstrapping. The following markers were different between the weaning success and failure groups: the ratio of standard deviations between the short-term and long-term heart rate variability in a Poincaré plot, sample entropy of ECG and PPG, α values of ECG, and respiratory impedance in the detrended fluctuation analysis. The area under the receiver operating characteristic curve of the model was 0.81 (95% confidence interval: 0.70-0.92). This combination of the biosignal data-based markers obtained during SBTs provides a promising tool to assist clinicians in determining the optimal extubation time.