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

Development of a Discharge Criteria Checklist for COVID-19 Patients From the Intensive Care Unit

This study aims to develop and validate a checklist of discharge readiness criteria for COVID-19 patients from the intensive care unit (ICU). We conducted a Delphi design study. The degree of agreement among 7 experts had been evaluated using the content validity index (CVI) through a 4-point Likert scale. The instrument was validated with 17 items. All the experts rated all items as very relevant which scored the item-CVI 1, which validates all checklist items. Using the mean of all items, the scale-CVI was calculated, and it was 1. This meant validation of the checklist as a whole. With regard to the overall checklist evaluation, the mean expert proportion of the instrument was 1, and the S-CVI/UA was 1. This discharge criteria checklist improves transition of care for COVID-19 patients and can help nurses, doctors, and academics to discharge COVID-19 patients from the ICU safely.

Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19

BACKGROUND

The exponential spread of coronavirus disease 2019 (COVID-19) causes unexpected economic burdens to worldwide health systems with severe shortages in hospital resources (beds, staff, equipment). Managing patients' length of stay (LOS) to optimize clinical care and utilization of hospital resources is very challenging. Projecting the future demand requires reliable prediction of patients' LOS, which can be beneficial for taking appropriate actions. Therefore, the purpose of this research is to develop and validate models using a multilayer perceptron-artificial neural network (MLP-ANN) algorithm based on the best training algorithm for predicting COVID-19 patients' hospital LOS.

METHODS

Using a single-center registry, the records of 1225 laboratory-confirmed COVID-19 hospitalized cases from February 9, 2020 to December 20, 2020 were analyzed. In this study, first, the correlation coefficient technique was developed to determine the most significant variables as the input of the ANN models. Only variables with a correlation coefficient at a P-value < 0.2 were used in model construction. Then, the prediction models were developed based on 12 training algorithms according to full and selected feature datasets (90% of the training, with 10% used for model validation). Afterward, the root mean square error (RMSE) was used to assess the models' performance in order to select the best ANN training algorithm. Finally, a total of 343 patients were used for the external validation of the models.

RESULTS

After implementing feature selection, a total of 20 variables were determined as the contributing factors to COVID-19 patients' LOS in order to build the models. The conducted experiments indicated that the best performance belongs to a neural network with 20 and 10 neurons in the hidden layer of the Bayesian regularization (BR) training algorithm for whole and selected features with an RMSE of 1.6213 and 2.2332, respectively.

CONCLUSIONS

MLP-ANN-based models can reliably predict LOS in hospitalized patients with COVID-19 using readily available data at the time of admission. In this regard, the models developed in our study can help health systems to optimally allocate limited hospital resources and make informed evidence-based decisions.

Discharge to post-acute care and other predictors of prolonged length of stay during the initial COVID-19 surge: a single site analysis

BACKGROUND

During the initial surge of coronavirus disease 2019 (COVID-19), health-care utilization fluctuated dramatically, straining acute hospital capacity across the USA and potentially contributing to excess mortality.

METHODS

This was an observational retrospective study of patients with COVID-19 admitted to a large US urban academic medical center during a 12-week COVID-19 surge in the Spring of 2020. We describe patterns in length of stay (LOS) over time. Our outcome of interest was prolonged LOS (PLOS), which we defined as 7 or more days. We performed univariate analyses of patient characteristics, clinical outcomes and discharge disposition to evaluate the association of each variable with PLOS and developed a final multivariate model via backward elimination, wherein all variables with a P-value above 0.05 were eliminated in a stepwise fashion.

RESULTS

The cohort included 1366 patients, of whom 13% died and 29% were readmitted within 30 days. The LOS (mean: 12.6) fell over time (P < 0.0001). Predictors of PLOS included discharge to a post-acute care (PAC) facility (odds ratio [OR]: 11.9, 95% confidence interval [CI] 2.6-54.0), uninsured status (OR 3.2, CI 1.1-9.1) and requiring intensive care and intubation (OR 18.4, CI 11.5-29.6). Patients had a higher readmission rate if discharged to PAC facilities (40%) or home with home health agency (HHA) services (38%) as compared to patients discharged home without HHA services (26%) (P < 0.0001).

CONCLUSION

Patients hospitalized with COVID-19 during a US COVID-19 surge had a PLOS and high readmission rate. Lack of insurance, an intensive care unit stay and a decision to discharge to a PAC facility were associated with a PLOS. Efforts to decrease LOS and optimize hospital capacity during COVID-19 surges may benefit from focusing on increasing PAC and HHA capacity and resources.

Clinical features of COVID-19 patients with rebound phenomenon after corticosteroid therapy

Rational

Corticosteroid therapy plays a key role in the treatment of COVID-19 patients with respiratory failure. However, a rebound phenomenon after steroid cessation rarely occurs. Here, we investigated the clinical features of patients with rebound after steroid therapy.

Methods

In total, 84 patients with COVID-19 treated with corticosteroids were enrolled and analysed retrospectively. A rebound was defined as when a patient’s respiratory status deteriorated after the cessation of corticosteroid therapy, without secondary bacterial infection.

Results

Subjects in the rebound group were more likely to having severe respiratory failure than those in the non-rebound group. While the duration of steroid therapy was longer in the rebound group (8 days vs 10 days, p=0.0009), the dosage of steroid and the timing of the start or termination of steroid therapy did not show any differences between the two groups (p=0.17 and 0.68, respectively). The values of soluble interleukin-2 receptor (sIL-2R) at the baseline and the values of C reactive protein (CRP) or lactate dehydrogenase (LDH) at the end of steroid therapy were significantly higher in the rebound group (937 vs 1336 U/mL; p=0.002, 0.63 vs 3.96 mg/dL; p=0.01 and 278 vs 451 IU/mL; p=0.01, respectively). No patient in the rebound group suffered from thromboses, and the causes of death were exacerbation of COVID-19, ventilator-associated pneumonia or sepsis. The prediction model using baseline features for the rebound phenomenon included four variables of age >68 years, required supplemental oxygen >5 L/min, lymphocyte counts <792 /µL and sIL-2R >1146 U/mL. The discrimination ability of this model was 0.906 (0.755–0.968).

Conclusion

These findings suggest that severe respiratory failure has a higher risk for the rebound phenomenon after the cessation of corticosteroids, and the values of sIL-2R, LDH and CRP are useful to assess the probability of developing rebound. A multivariate model was developed to predict rebound risk, which showed acceptable discrimination ability.

Costs for a hospital stay: another lesson learned from the COVID-19 pandemic

Background and aim: After two years of pandemic, planning and budgeting for use of healthcare resources and services is very important. Inpatient COVID-19 hospitalizations costs, regardless of ICD-10 procedure codes, in a Covid-19 support military hospital were analyzed. Methods: The national protocol for the treatment of Covid-19 infection was applied. The costs for laboratory tests, drugs, protection equipment and radiological investigations (imaging techniques such as computed-tomography or radiography), hospitalization days and food were assessed. Results: In our hospital, from August 2020 through June 2021, 241 patients were hospitalized with COVID-19: mean age 59.92±7.8 years, 46% men, 26% military personnel, 11.57±3 days of hospitalization; two third of patients had moderate and severe forms of COVID-19. The main manifestations were: 69% respiratory (18% with severe pneumonia), 3.3% cardiac (2.9% with pulmonary embolism, diagnosed by computed tomography angiography), 28% digestive and 33% psychiatric (most commonly anxiety). The average estimated costs were about 3000€/patient, without significant differences based on disease severity. Equipment costs were 2 times higher than for drugs and 3 times than for laboratory tests. Conclusions: In a Covid-19 support military hospital that cared for patients with predominantly moderate forms of COVID-19, the costs for equipment were much higher than those for treatment. New criteria for hospitalization of these forms of COVID-19 deserve to be analyzed in order to avoid useless costs

Retrospective Study of Aging and Sex-Specific Risk Factors of COVID-19 with Hypertension in China

Background

Coronavirus disease 2019 (COVID-19) has been a global threat that pushes healthcare to its limits. Hypertension is one of the most common risk factors for cardiovascular complications in COVID-19 and is strongly associated with disease severity and mortality. To date, clinical mechanisms by which hypertension leads to increased risk in COVID-19 are still unclear. Furthermore, additional factors might increase these risks, such as the consideration of age and sex, which are of interest when in search of personalized treatments for hypertensive COVID-19 patients.

Methods

We conducted a retrospective cohort study of 543 COVID-19 patients in seven provinces of China to examine the epidemiological and clinical characteristics of COVID-19 in this population and to determine risk factors of hypertensive COVID-19 patients. We also used univariable and multivariable logistic regression methods to explore the risk factors associated with hypertensive COVID-19 patients in different age and sex subgroups.

Results

Among the enrolled COVID-19 patients, the median age was 47 years (interquartile range (IQR) 34.0–57.0), and 99 patients (18.23%) were over 60 years old. With regard to comorbidities, 91 patients (16.75%) were diagnosed with hypertension, followed by diabetes, coronary disease, and cerebrovascular disease. Of the hypertensive COVID-19 patients, 51 (56.04%) were male. Multivariable analysis showed that old age, comorbid diabetes or coronary heart disease on admission, increased D-dimer, increased glucose, and decreased lymphocyte count were independent risk factors associated with hypertensive COVID-19 patients. Elevated total bilirubin (odds ratio [OR]: 1.014, 95% confidence interval [CI]: 0.23–1.05; p = 0.043) and triglycerides (OR: 1.173, 95% CI: 0.049–1.617; p = 0.007) were found to be associated with elderly hypertensive COVID-19 patients. In addition, we found that decreased lymphocytes, basophil, high-density lipoprotein, and increased fibrinogen and creatinine were related to a higher risk of disease severity in male patients. The most common abnormal clinical findings pertaining to female hypertensive COVID-19 patients were hemoglobin, total bile acid, total protein, and low-density lipoprotein.

Conclusions

Factors associated with increased risk of hypertensive COVID-19 patients were identified. Results to the different age and sex subgroups in our study will allow for better possible personalized care and also provide new insights into specific risk stratification, disease management, and treatment strategies for COVID-19 patients with hypertension in the future.

Clinical Manifestations of COVID-19 Patients with Comorbid and Non-comorbid at Dr. Soetomo Hospital, Surabaya

Background : Covid-19 has been declared a global health emergency. Reports of thousands of cases with morbidity and mortality continue to increase every day. The clinical course of patients with comorbidities influences the prognosis and progression of the covid-19 disease. Hypertension is the most common cormorbidity of covid-19 patients with long hospitalizations. Objective : This study aimed to determine the clinical differences between covid-19 patients cormobid and non cormobid . Methods : The study was conducted retrospectively through samples of medical records of inpatients for the period June 1, 2021 – August 31, 2021. The samples were divided into comorbid and non-cormobid groups; each totaling 130 medical records. The sample of the comorbid group was selected by simple random; while the non-cormobid group with the matching process. Data were analyzed using t-test and Wilcoxon. Results : The most common kormobid is hypertension with clinical manifestations of cough, fever, headache, runny nose, painful swallowing, anosmia, shortness of breath, nausea, vomiting and diarrhea. The average length of stay for patients with comorbidities was 21 days and without comorbidities 14 days. The test results showed that there were clinical differences between patients with comorbid and non-cormobid patients with p value = 0.0000 (p>0.05) and there was a difference in length of stay with p-value = 0.001 (p>0.05). Conclusion : The clinical difference between covid-19 patients comorbid and non cormobid lies in the symptoms of headache with a longer duration of treatment, which is 22 days. More intensive treatment and care is needed for covid-19 patients with comorbid hypertension. Keywords : clinical manifestation; covid-19; comorbid; length of treatment.

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.

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 (β: −0.32 per day, standard error = 0.12, p = 0.0099). (4) Conclusions: Elevated MDW was associated with a prolonged LOS.

Potential Risk Factors for Length of Hospitalization in COVID-19 Patients: A Cross-sectional Study

Background: Identifying the potential risk factors of the length of stay in hospital (LOSH) in COVID-19 patients could help the health system meet future demand for hospital beds. Objectives: This study aimed to determine the factors affecting the length of stay in hospital in COVID-19 patients in Hamadan, the west of Iran. Methods: This cross-sectional study recruited 512 hospitalized COVID-19 patients in Hamadan city. Demographic, clinical, and medical laboratory characteristics of the patients and their survival status were assessed by a checklist. Univariate and multiple negative binomial regressions were used by Stata 12. Results: The median hospitalization length for COVID-19 patients was five days (range: 0 to 47). In the discharged patients, the adjusted incidence rate ratios (95% CI) of LOSH for females, rural residents, patients with a history of diabetes and cardiovascular disease, SPO2 less than 88%, prothrombin time higher than 13 s, platelet count lower than 130 × 1000 µL, blood sugar higher than 105 mg/dL, and intensive care unit experience were 1.16 (1.03, 1.44), 1.22 (1.03, 1.44), 1.43 (1.07, 1.92), 1.41 (1.23, 1.61), 0.82 (0.71, 0.93), 1.32 (1.11, 1.56), 1.18 (1.03, 1.36), and 1.85 (1.59, 2.17) compared to their references, respectively. Conclusions: Our study added new insight into LOSH determining factors that could be used for future planning in combating the need for hospital beds. The present study revealed that some demographic, social, and clinical variables could increase the IRR of a more extended hospital stay.

Hospitalization budget impact during the COVID-19 pandemic in Spain

OBJECTIVES

The aim was to determine the direct impact of the COVID-19 pandemic on Spain's health budget.

METHODS

Budget impact analyses based on retrospective data from patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) admitted to a Spanish hospital between February 26 and May 21, 2020. Direct medical costs from the perspective of the hospital were calculated. We analyzed diagnostic tests, drugs, medical and nursing care, and isolation ward and ICU stays for three cohorts: patients seen in the emergency room only, hospitalized patients who tested positive for SARS-CoV-2, and patients who tested negative.

RESULTS

The impact on the hospital's budget for the 3 months was calculated at €15,633,180, 97.4% of which was related to health care and hospitalization. ICU stays accounted for 5.3% of the total costs. The mean cost per patient was €10,744. The main costs were staffing costs (10,131 to 11,357 €/patient for physicians and 10,274 to 11,215 €/patient for nurses). Scenario analysis showed that the range of hospital expenditure was between €14,693,256 and €16,524,924. The median impact of the pandemic on the Spanish health budget in the sensitivity analysis using bootstrapped individual data was €9357 million (interquartile range [IQR], 9071 to 9689) for the conservative scenario (113,588 hospital admissions and 11,664 ICU admissions) and €10,385 million (IQR, 110,030 to 10,758) for the worst-case scenario (including suspected cases).

CONCLUSION

The impact of COVID-19 on the Spanish public health budget (12.3% of total public health expenditure) is greater than multiple sclerosis, cancer and diabetes cost.

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

Background

Accurate risk stratification of critically ill patients with coronavirus disease 2019 (COVID-19) is essential for optimizing resource allocation, delivering targeted interventions, and maximizing patient survival probability. Machine learning (ML) techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical care.

Methods

We retrieved data on patients with COVID-19 admitted to an intensive care unit (ICU) between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit (RISC-19-ICU) registry. We applied the Extreme Gradient Boosting (XGBoost) algorithm to the data to predict as a binary outcome the increase or decrease in patients' Sequential Organ Failure Assessment (SOFA) score on day 5 after ICU admission. The model was iteratively cross-validated in different subsets of the study cohort.

Results

The final study population consisted of 675 patients. The XGBoost model correctly predicted a decrease in SOFA score in 320/385 (83%) critically ill COVID-19 patients, and an increase in the score in 210/290 (72%) patients. The area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model (0.86 vs. 0.69, P < 0.01 [paired t-test with 95% confidence interval]).

Conclusions

The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems (CDSSs) aimed at optimizing available resources.

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study

Assessing the length of hospital stay (LOS) in patients with coronavirus disease 2019 (COVID-19) pneumonia is helpful in optimizing the use efficiency of hospital beds and medical resources and relieving medical resource shortages. This retrospective cohort study of 97 patients was conducted at Beijing You'An Hospital between January 21, 2020, and March 21, 2020. A multivariate Cox proportional hazards regression based on the smallest Akaike information criterion value was used to select demographic and clinical variables to construct a nomogram. Discrimination, area under the receiver operating characteristic curve (AUC), calibration, and Kaplan–Meier curves with the log-rank test were used to assess the nomogram model. The median LOS was 13 days (interquartile range [IQR]: 10–18). Age, alanine aminotransferase, pneumonia, platelet count, and PF ratio (PaO₂/FiO₂) were included in the final model. The C-index of the nomogram was 0.76 (95%confidence interval [CI] = 0.69–0.83), and the AUC was 0.88 (95%CI = 0.82–0.95). The adjusted C-index was 0.75 (95%CI = 0.67–0.82) and adjusted AUC 0.86 (95%CI = 0.73–0.95), both after 1000 bootstrap cross internal validations. A Brier score of 0.11 (95%CI = 0.07–0.15) and adjusted Brier score of 0.130 (95%CI = 0.07–0.20) for the calibration curve showed good agreement. The AUC values for the nomogram at LOS of 10, 20, and 30 days were 0.79 (95%CI = 0.69–0.89), 0.89 (95%CI = 0.83–0.96), and 0.96 (95%CI = 0.92–1.00), respectively, and the high fit score of the nomogram model indicated a high probability of hospital stay. These results confirmed that the nomogram model accurately predicted the LOS of patients with COVID-19. We developed and validated a nomogram that incorporated five independent predictors of LOS. If validated in a future large cohort study, the model may help to optimize discharge strategies and, thus, shorten LOS in patients with COVID-19.

Discharge Practices for COVID-19 Patients: Rapid Review of Published Guidance and Synthesis of Documents and Practices at 22 US Academic Medical Centers

BACKGROUND

There are currently no evidence-based guidelines that provide standardized criteria for the discharge of COVID-19 patients from the hospital.

OBJECTIVE

To address this gap in practice guidance, we reviewed published guidance and collected discharge protocols and procedures to identify and synthesize common practices.

DESIGN

Rapid review of existing guidance from US and non-US public health organizations and professional societies and qualitative review using content analysis of discharge documents collected from a national sample of US academic medical centers with follow-up survey of hospital leaders SETTING AND PARTICIPANTS: We reviewed 65 websites for major professional societies and public health organizations and collected documents from 22 Academic Medical Centers (AMCs) in the US participating in the HOspital MEdicine Reengineering Network (HOMERuN).

RESULTS

We synthesized data regarding common practices around 5 major domains: (1) isolation and transmission mitigation; (2) criteria for discharge to non-home settings including skilled nursing, assisted living, or homeless; (3) clinical criteria for discharge including oxygenation levels, fever, and symptom improvement; (4) social support and ability to perform activities of daily living; (5) post-discharge instructions, monitoring, and follow-up.

LIMITATIONS

We used streamlined methods for rapid review of published guidance and collected discharge documents only in a focused sample of US academic medical centers.

CONCLUSION

AMCs studied showed strong consensus on discharge practices for COVID-19 patients related to post-discharge isolation and transmission mitigation for home and non-home settings. There was high concordance among AMCs that discharge practices should address COVID-19-specific factors in clinical, functional, and post-discharge monitoring domains although definitions and details varied.

Antibiotic prescribing in patients with COVID-19: rapid review and meta-analysis

Background

The proportion of patients infected with SARS-CoV-2 that are prescribed antibiotics is uncertain, and may contribute to patient harm and global antibiotic resistance.

Objective

The aim was to estimate the prevalence and associated factors of antibiotic prescribing in patients with COVID-19.

Data Sources

We searched MEDLINE, OVID Epub and EMBASE for published literature on human subjects in English up to June 9 2020.

Study Eligibility Criteria

We included randomized controlled trials; cohort studies; case series with ≥10 patients; and experimental or observational design that evaluated antibiotic prescribing.

Participants

The study participants were patients with laboratory-confirmed SARS-CoV-2 infection, across all healthcare settings (hospital and community) and age groups (paediatric and adult).

Methods

The main outcome of interest was proportion of COVID-19 patients prescribed an antibiotic, stratified by geographical region, severity of illness and age. We pooled proportion data using random effects meta-analysis.

Results

We screened 7469 studies, from which 154 were included in the final analysis. Antibiotic data were available from 30 623 patients. The prevalence of antibiotic prescribing was 74.6% (95% CI 68.3–80.0%). On univariable meta-regression, antibiotic prescribing was lower in children (prescribing prevalence odds ratio (OR) 0.10, 95% CI 0.03–0.33) compared with adults. Antibiotic prescribing was higher with increasing patient age (OR 1.45 per 10 year increase, 95% CI 1.18–1.77) and higher with increasing proportion of patients requiring mechanical ventilation (OR 1.33 per 10% increase, 95% CI 1.15–1.54). Estimated bacterial co-infection was 8.6% (95% CI 4.7–15.2%) from 31 studies.

Conclusions

Three-quarters of patients with COVID-19 receive antibiotics, prescribing is significantly higher than the estimated prevalence of bacterial co-infection. Unnecessary antibiotic use is likely to be high in patients with COVID-19.

A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China

BACKGROUND

Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients.

METHODS

Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram.

RESULTS

Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P = 0.02). Baseline activated partial thromboplastin time (APTT) > 30 s was correlated with longer isolation length (P = 0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power.

CONCLUSIONS

Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length.

Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review

BACKGROUND

This study is aimed at reviewing the published literature on the prognostic role of serum procalcitonin (PCT) in COVID-19 cases.

DATA RETRIEVAL

We systematically reviewed the literature available on PubMed, MEDLINE, LitCovid NLM, and WHO: to assess the utility of PCT in prognosis of coronavirus disease. Scrutiny for eligible studies comprising articles that have evaluated the prognostic utility of PCT and data compilation was undertaken by two separate investigators. Original articles in human subjects reporting the prognostic role of PCT in adult COVID-19 patients were included. The Quality in Prognosis Studies (QUIPS) tool was utilized to assess the strength of evidence. Results were reported as narrative syntheses.

RESULTS

Out of the total 426 citations, 52 articles passed through screening. The quality of evidence and methodology of included studies was overall acceptable. The total sample size of the studies comprised of 15,296 COVID-19-positive subjects. Majority of the studies were from China, i.e., 40 (77%). The PCT cut-off utilized was 0.05 ng/mL by 18 (35%) studies, followed by 0.5 ng/mL by 9 (17.5%). Eighty five percent (n = 44) studies reported statistically significant association (p value < 0.05) between PCT and severity.

CONCLUSION

Procalcitonin appears as a promising prognostic biomarker of COVID-19 progression in conjunction with the clinical context.

HOW TO CITE THIS ARTICLE

Ahmed S, Jafri L, Hoodbhoy Z, Siddiqui I. Prognostic Value of Serum Procalcitonin in COVID-19 Patients: A Systematic Review. Indian J Crit Care Med 2021;25(1):77-84.

Patient Flow Dynamics in Hospital Systems During Times of COVID-19: Cox Proportional Hazard Regression Analysis

Objectives: The present study is aimed at estimating patient flow dynamic parameters and requirement for hospital beds. Second, the effects of age and gender on parameters were evaluated.

Patients and Methods: In this retrospective cohort study, 987 COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India). The survival analysis was carried out from February 29 through May 19, 2020, for two hazards: Hazard 1 was hospital discharge, and Hazard 2 was hospital death. The starting point for survival analysis of the two hazards was considered to be hospital admission. The survival curves were estimated and additional effects of age and gender were evaluated using Cox proportional hazard regression analysis.

Results: The Kaplan Meier estimates of lengths of hospital stay (median = 10 days, IQR = 5–15 days) and median survival rate (more than 60 days due to a large amount of censored data) were obtained. The Cox model for Hazard 1 showed no significant effect of age and gender on duration of hospital stay. Similarly, the Cox model 2 showed no significant difference of age and gender on survival rate. The case fatality rate of 8.1%, recovery rate of 78.8%, mortality rate of 0.10 per 100 person-days, and hospital admission rate of 0.35 per 100,000 person-days were estimated.

Conclusion: The study estimates hospital bed requirements based on median length of hospital stay and hospital admission rate. Furthermore, the study concludes there are no effects of age and gender on average length of hospital stay and no effects of age and gender on survival time in above-60 age groups.

COVID-19 in Older Adults: A Series of 76 Patients Aged 85 Years and Older with COVID-19

BACKGROUND

Clinical presentation and risk factors of death in COVID-19 in oldest adults have not been well characterized.

OBJECTIVES

To describe clinical features and outcome of COVID-19 in patients older than 85 years and study risk factors for mortality.

DESIGN

Prospective cohort.

PARTICIPANTS AND SETTING

Patients aged 85 years and older, admitted in noncritical care units at the University Hospital Lariboisière Fernand-Widal (Paris, France) for confirmed severe acute respiratory syndrome coronavirus 2 infection were included and followed up for 21 days.

MEASUREMENTS

Clinical and laboratory findings were collected. Cox survival analysis was performed to explore factors associated with death.

RESULTS

From March 14 to April 11, 2020, 76 patients (median age = 90 (86-92) years; women = 55.3%) were admitted for confirmed COVID-19. Of the patients, 64.5% presented with three or more comorbidities. Most common symptoms were asthenia (76.3%), fever (75.0%) and confusion and delirium (71.1%). An initial fall was reported in 25.0% of cases, and digestive symptoms were reported in 22.4% of cases. COVID-19 was severe in 51.3% of cases, moderate in 32.9%, and mild in 15.8%. Complications included acute respiratory syndrome (28.9%), cardiac decompensation (14.5%), and hypotensive shock (9.0%). Fatality at 21 days was 28.9%, after a median course of disease of 13 (8-17) days. Males were overrepresented in nonsurvivors (68.2%). In survivors, median length of stay was 12 (9-19.5) days. Independent predictive factors of death were C-reactive protein level at admission and lymphocyte count at nadir.

CONCLUSION

Specific clinical features, multiorgan injury, and high case fatality rate are observed in older adults with COVID-19. However, rapid diagnosis, appropriate care, and monitoring seem to improve prognosis.

Association of cardiac biomarkers and comorbidities with increased mortality, severity, and cardiac injury in COVID-19 patients: A meta-regression and decision tree analysis

BACKGROUND

Coronavirus disease-2019 (COVID-19) has a deleterious effect on several systems, including the cardiovascular system. We aim to systematically explore the association of COVID-19 severity and mortality rate with the history of cardiovascular diseases and/or other comorbidities and cardiac injury laboratory markers.

METHODS

The standardized mean difference (SMD) or odds ratio (OR) and 95% confidence intervals (CIs) were applied to estimate pooled results from the 56 studies. The prognostic performance of cardiac markers for predicting adverse outcomes and to select the best cutoff threshold was estimated by receiver operating characteristic curve analysis. Decision tree analysis by combining cardiac markers with demographic and clinical features was applied to predict mortality and severity in patients with COVID-19.

RESULTS

A meta-analysis of 17 794 patients showed patients with high cardiac troponin I (OR = 5.22, 95% CI = 3.73-7.31, P < .001) and aspartate aminotransferase (AST) levels (OR = 3.64, 95% CI = 2.84-4.66, P < .001) were more likely to develop adverse outcomes. High troponin I more than 13.75 ng/L combined with either advanced age more than 60 years or elevated AST level more than 27.72 U/L was the best model to predict poor outcomes.

CONCLUSIONS

COVID-19 severity and mortality are complicated by myocardial injury. Assessment of cardiac injury biomarkers may improve the identification of those patients at the highest risk and potentially lead to improved therapeutic approaches.

Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China

Background

Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).

Methods

Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.

Results

The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.

Conclusion

We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

Corticosteroids in COVID-19: Is it Rational? A Systematic Review and Meta-Analysis

Due to a lack of definitive treatment, many drugs were repurposed for Coronavirus disease (COVID-19) treatment; among them, corticosteroid is one. However, its benefit or harm while treating COVID-19 is not fully studied. Thus, we conducted this meta-analysis to assess the rationality on the use of corticosteroids in COVID-19. Pubmed, Medline, Clinicaltrials.gov, Cochrane library, and Preprint publisher were searched. In the qualitative syntheses, 41, and quantitative studies, 40, were included using PRISMA guidelines. Assessment of heterogeneity was done using the I-squared (I 2) test and random/fixed effect analysis was done to determine the odds/risk ratio. We found severely ill COVID-19 patients almost 5 (OR 4.78, 2.76-8.26) times higher odds of getting corticosteroids during their treatment. Similarly, the odds for corticosteroids in addition to standard of care (SOC) were approximately 4 (OR 4.09, 1.89-8.84) times higher among intensive care unit (ICU) patients than non-ICU ones. A higher mortality risk with the corticosteroid-receiving group compared with the SOC alone (RR 2.01, 1.12-3.63) was observed. Neither increased discharge rate (RR 0.79, 0.63-0.99) nor recovery/improvement rate was shown among the corticosteroid-receiving group (OR 0.24, 0.13-0.43). Approximately, the overall 4-day longer hospital stay was found among the treatment groups (MD 4.19, 2.57-5.81). For the negative conversion of reverse transcription-polymerase chain reaction (RT-PCR), approximately a 3-day (MD 2.42, 1.31-3.53) delay was observed with corticosteroid treatment cases. Our study concludes that more severe and critically ill patients tend to get corticosteroids, and the mortality risk increases with the use of corticosteroids. With the use of corticosteroids, delayed recovery and a longer hospital stay were observed.

D-dimer level in COVID-19 infection: a systematic review

INTRODUCTION

COVID-19 disease has spread worldwide from December 2019 to the present day; the early stage of this disease can be associated with high D-dimer, prolonged PT, and elevated levels of fibrinogen, indicating activation of coagulation pathways and thrombosis. In this article, we analyze the levels of D-dimer in patients with COVID-19.

AREA COVERED

In the current study, three databases, PubMed, Scopus, Web of Science, searched using related keywords and information extracted from articles such as location, sample size, gender, age, coagulation test values, patient results, and disease severity.

EXPERT OPINION

D-dimer level is one of the measures used in patients to detect thrombosis. Studies have reported an increase in D-dimer and fibrinogen concentrations in the early stages of COVID-19 disease a 3 to 4-fold rise in D-dimer levels is linked to poor prognosis. In addition, underlying diseases such as diabetes, cancer, stroke, and pregnancy may trigger an increase in D-dimer levels in COVID-19 patients. Measuring the level of D-dimer and coagulation parameters from the early stage of the disease can also be useful in controlling and managing of COVID-19 disease.

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.

Identification of risk factors for mortality associated with COVID-19

OBJECTIVES

Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).

METHODS

This study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.

RESULTS

A total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.

CONCLUSIONS

Our study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.

COVID-19 and COPD

As of 11 July, 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus disease 2019 (COVID-19) pandemic has infected over 12.7 million people around the world and caused more than 560,000 deaths [1]. Given the devastating impact that COVID-19 can have on the lung, it is natural to fear for patients with underlying COPD. Estimating their excess risk for contracting COVID-19 and, in particular, its more severe respiratory manifestations has been a challenging exercise in this pandemic for various reasons. First, the reporting on cases has concentrated on hospitalised and intensive care unit (ICU) patients, rather than on mild, outpatient cases. This is in part also due to the variability in testing strategies across the world, where some nations with stricter testing requirements and scarce testing resources have focused on testing only those requiring hospitalisation.

COPD patients have increased risk of severe pneumonia and poor outcomes when they develop COVID-19. This may be related to poor underlying lung reserves or increased expression of ACE-2 receptor in small airways.https://bit.ly/37dSB8l