Development of a model by LASSO to predict hospital length of stay (LOS) in patients with the SARS-Cov-2 omicron variant.

The length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients' APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778*Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT).

Plasma Glycated Hemoglobin A1c Could Predict 30-Day All-cause Mortality of Intensive Care Unit Patients with Hyperglycemia

Background: An increased serum glucose level is a common finding among patients admitted to hospital with acute illness, including the intensive care unit (ICU), even without a history of previous diabetes mellitus (DM]. Glycated hemoglobin (HbAlc) is not only a diagnostic tool for DM but may also has prognostic value for diabetic and non-diabetic populations. Objective(s): To assess the relationship between HbA1c level on admission and clinical outcome among patients admitted to the ICU due to cardiopulmonary disorders with hyperglycemia. Method(s): Patients consecutively admitted to the ICU due to cardiopulmonary disorders who presented with hyperglycemia at admission were evaluated during a 6-month period. HbAlc and serum glucose levels were tested on admission and during the first 24-48 hours of hospitalization. Patients were divided according to HbA1c and compared in term of demographics. We evaluated the effect of HbA1c levels at admission on the clinical outcomes. Result(s): Of patients with cardiopulmonary disorders who presented with hyperglycemia at admission to the ICU, 73 had HbA1c levels 6%, 92 had HbA1c levels < 6%: 63/165 (38.2%) known as diabetic patients. The 30-day all-cause mortality was higher in the group with high HbA1c levels;38/73 vs. 32/98 (P= 0.02). Increased length of stay in the ICU and Acute Physiology and Chronic Health Evaluation II (APACHE II) score were associated with HbA1 c 6% (P < 0.022 and P < 0.026), respectively Conclusion(s): HbAlc 6% has an important clinical prognostic value among diabetic and non-diabetic patients with cardiopulmonary disorders and hyperglycemia. Copyright © 2022 Israel Medical Association. All rights reserved.

Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia.

The COVID-19 virus has spread rapidally throughout the world. Managing resources is one of the biggest challenges that healthcare providers around the world face during the pandemic. Allocating the Intensive Care Unit (ICU) beds' capacity is important since COVID-19 is a respiratory disease and some patients need to be admitted to the hospital with an urgent need for oxygen support, ventilation, and/or intensive medical care. In the battle against COVID-19, many governments utilized technology, especially Artificial Intelligence (AI), to contain the pandemic and limit its hazardous effects. In this paper, Machine Learning models (ML) were developed to help in detecting the COVID-19 patients' need for the ICU and the estimated duration of their stay. Four ML algorithms were utilized: Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ensemble models were trained and validated on a dataset of 895 COVID-19 patients admitted to King Fahad University hospital in the eastern province of Saudi Arabia. The conducted experiments show that the Length of Stay (LoS) in the ICU can be predicted with the highest accuracy by applying the RF model for prediction, as the achieved accuracy was 94.16%. In terms of the contributor factors to the length of stay in the ICU, correlation results showed that age, C-Reactive Protein (CRP), nasal oxygen support days are the top related factors. By searching the literature, there is no published work that used the Saudi Arabia dataset to predict the need for ICU with the number of days needed. This contribution is hoped to pave the path for hospitals and healthcare providers to manage their resources more efficiently and to help in saving lives.

Prediction of Length of Stay in the Emergency Department for COVID-19 Patients: A Machine Learning Approach

The coronavirus disease (COVID-19) outbreak has become a global public health threat. The influx of COVID-19 patients has prolonged the length of stay (LOS) in the emergency department (ED) in the United States. Our objective is to develop a reliable prediction model for COVID-19 patient ED LOS and identify clinical factors, such as age and comorbidities, associated with LOS within a “4-hour target.”Data were collected from an urban, demographically diverse hospital in Detroit for all COVID-19 patients’ED presentations from March 16 to December 29, 2020. We trained four machine learning models, namely logistic regression (LR), gradient boosting (GB), decision tree (DT), and random forest (RF), across different data processing stages to predict COVID-19 patients with an ED LOS of less than or greater than 4 hours. The analysis is inclusive of 3,301 COVID-19 patients with known ED LOS, and 17 significant clinical factors were incorporated. The GB model outperformed the baseline classifier (LR) and tree-based classifiers (DT and RF) with an accuracy of 85% and F1-score of 0.88 for predicting ED LOS in the testing data. No significant accuracy gains were achieved through further splitting. This study identified key independent factors from a combination of patient demographics, comorbidities, and ED operational data that predicted ED stay in patients with prolonged COVID-19. The prediction framework can serve as a decision-support tool to improve ED and hospital resource planning and inform patients about better ED LOS estimations. Author

Fever, Tachypnea, and Monocyte Distribution Width Predicts Length of Stay for Patients with COVID-19: A Pioneer Study.

(1) Background: Our study investigated whether monocyte distribution width (MDW) could be used in emergency department (ED) settings as a predictor of prolonged length of stay (LOS) for patients with COVID-19. (2) Methods: A retrospective cohort study was conducted; patients presenting to the ED of an academic hospital with confirmed COVID-19 were enrolled. Multivariable logistic regression models were used to obtain the odds ratios (ORs) for predictors of an LOS of >14 days. A validation study for the association between MDW and cycle of threshold (Ct) value was performed. (3) Results: Fever > 38 °C (OR: 2.82, 95% CI, 1.13-7.02, p = 0.0259), tachypnea (OR: 4.76, 95% CI, 1.67-13.55, p = 0.0034), and MDW ≥ 21 (OR: 5.67, 95% CI, 1.19-27.10, p = 0.0269) were robust significant predictors of an LOS of >14 days. We developed a new scoring system in which patients were assigned 1 point for fever > 38 °C, 2 points for tachypnea > 20 breath/min, and 3 points for MDW ≥ 21. The optimal cutoff was a score of ≥2. MDW was negatively associated with Ct value (ß: -0.32 per day, standard error = 0.12, p = 0.0099). (4) Conclusions: Elevated MDW was associated with a prolonged LOS.

Increased Serum Aminotransferase Activity and Clinical Outcomes in Coronavirus Disease 2019.

AIM: Elevation of hepatic aminotransferases (aspartate aminotransferase [AST]/alanine aminotransferase [ALT]) is commonly noted among COVID-19 patients. It is unclear if they can predict the clinical outcomes among hospitalized COVID-19 patients. We aim to assess if elevations in AST/ALT were associated with poor outcomes in hospitalized COVID-19 patients. METHODS: We retrospectively evaluated hospitalized COVID-19 patients with clinically significant elevated aminotransferases (defined as >2 times upper limit of normal) and compared them with COVID-19 patients without an elevation in aminotransferases. RESULTS: The prevalence of elevation in AST/ALT was found to be 13.7% (20/145). The two groups were similar in baseline demographics, comorbidities, and the majority of laboratory tests. There was no difference in the mortality (50% vs. 36.8%, P = 0.32) and median hospital stay (7 days vs. 7 days, P = 0.78). However, there was a statistically significant increase in the rates of mechanical ventilation among elevated aminotransferases group compared with individuals without elevation (50% vs. 24%, P = 0.028). However, this difference was not observed after adjusting for inflammatory markers such as ferritin, lactate dehydrogenase, and lactic acid levels. CONCLUSION: Elevated aminotransferases among hospitalized COVID-19 patients is associated with higher rates of mechanical ventilation but did not achieve statistical significance after controlling for inflammatory markers. Also, patients with elevated aminotransferases did not have higher rates of mortality or prolonged length of stay.

Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay.

BACKGROUND: The epidemic of Coronavirus Disease 2019 (COVID-19) has become a global health emergency, but the clinical characteristics of COVID-19 are not fully described. We aimed to describe the clinical characteristics of COVID-19 outside of Wuhan city; and to develop a multivariate model to predict the risk of prolonged length of stay in hospital (ProLOS). METHODS: The study was conducted in a tertiary care hospital in Zhejiang province from January to February 20, 2020. Medical records of all confirmed cases of COVID-19 were retrospectively reviewed. Patients were categorized into the ProLOS and non-ProLOS groups by hospital length of stay greater and less than 14 days, respectively. Conventional descriptive statistics were applied. Multivariate regression model was built to predict the risk of ProLOS, with variables selected using stepwise approach. RESULTS: A total of 75 patients with confirmed COVID-19 were included for quantitative analysis, including 25 (33%) patients in the ProLOS group. ProLOS patients were more likely to have history of traveling to Wuhan (68% vs. 28%; P=0.002). Patients in the ProLOS group showed lower neutrophil counts [median (IQR): 2.50 (1.77-3.23) ×109/L vs. 2.90 (2.21-4.19) ×109/L; P=0.048], higher partial thrombin time (PT) (13.42±0.63 vs. 13.10±0.48 s; P=0.029), lower D-Dimer [0.26 (0.22-0.46) vs. 0.44 (0.32-0.84) mg/L; P=0.012]. There was no patient died and no severe case in our cohort. The overall LOS was 11 days (IQR, 5-15 days). The median cost for a hospital stay was 7,388.19 RMB (IQR, 5,085.39-11,145.44). The prediction model included five variables of procalcitonin, heart rate, epidemiological history, lymphocyte count and cough. The discrimination of the model was 84.8% (95% CI: 75.3% to 94.4%). CONCLUSIONS: Our study described clinical characteristics of COVID-19 outside of Wuhan city and found that the illness was less severe than that in the core epidemic region. A multivariate model was developed to predict ProLOS, which showed good discrimination.