Risk factors of early mortality among COVID-19 deceased patients in Addis Ababa COVID-19 care centers, Ethiopia

Analysis of six consecutive waves of ICU-admitted COVID-19 patients: key findings and insights from a Portuguese population

Identifying high-risk patients, particularly in intensive care units (ICUs), enhances treatment and reduces severe outcomes. Since the pandemic, numerous studies have examined COVID-19 patient profiles and factors linked to increased mortality. Despite six pandemic waves, to the best of our knowledge, there is no extensive comparative analysis of patients' characteristics across these waves in Portugal. Thus, we aimed to analyze the demographic and clinical features of 1041 COVID-19 patients admitted to an ICU and their relationship with the different SARS-Cov-2 variants in Portugal. Additionally, we conducted an in-depth examination of factors contributing to early and late mortality by analyzing clinical data and laboratory results from the first 72 h of ICU admission. Our findings revealed a notable decline in ICU admissions due to COVID-19, with the highest mortality rates observed during the second and third waves. Furthermore, immunization could have significantly contributed to the reduction in the median age of ICU-admitted patients and the severity of their conditions. The factors contributing to early and late mortality differed. Age, wave number, D-dimers, and procalcitonin were independently associated with the risk of early death. As a measure of discriminative power for the derived multivariable model, an AUC of 0.825 (p < 0.001; 95% CI, 0.719-0.931) was obtained. For late mortality, a model incorporating age, wave number, hematologic cancer, C-reactive protein, lactate dehydrogenase, and platelet counts resulted in an AUC of 0.795 (p < 0.001; 95% CI, 0.759-0.831). These findings underscore the importance of conducting comprehensive analyses across pandemic waves to better understand the dynamics of COVID-19.

Survival of hospitalised COVID-19 patients in Hawassa, Ethiopia: a cohort study

The COVID-19 pandemic, caused by SARS-CoV-2, led to 622,119,701 reported cases and 6,546,118 deaths. Most studies on COVID-19 patients in hospitals are from high-income countries, lacking data for developing countries such as Ethiopia.This study assesses clinical features, demographics, and risk factors for in-hospital mortality in Hawassa, Ethiopia. The research cohort comprises 804 cases exhibiting clinical diagnoses and/or radiological findings and indicative of symptoms consistent with COVID-19 at Hawassa University Comprehensive Specialized Hospital from September 24, 2020, to November 26, 2021. In-hospital mortality rate was predicted using Cox regression. The median age was 45 years, with males making up 64.1% of the population. 173 (21.5%) fatalities occurred, with 125 (72.3%) among males. Male patients had higher mortality rates than females. Severe and critical cases were 24% and 21%. 49.1% had at least one comorbidity, with 12.6% having multiple. Common comorbidities were diabetes (15.9%) and hypertension (15.2%). The Cox regression in Ethiopian COVID-19 patients found that factors like gender, advanced age group, disease severity, symptoms upon admission, shortness of breath, sore throat, body weakness, hypertension, diabetes, multiple comorbidities, and prior health facility visits increased the risk of COVID-19 death, similar to high-income nations. However, in Ethiopia, COVID-19 patients were young and economically active. Patients with at least one symptom had reduced death risk. As a conclusion, COVID-19 in Ethiopia mainly affected the younger demographic, particularly economically active individuals. Early detection can reduce the risk of mortality. Prompt medical attention is essential, especially for individuals with comorbidities. Further research needed on diabetes and hypertension management to reduce mortality risk. Risk factors identified at admission play a crucial role in guiding clinical decisions for intensive monitoring and treatment. Broader risk indicators help prioritize patients for allocation of hospital resources, especially in regions with limited medical facilities. Government's focus on timely testing and strict adherence to regulations crucial for reducing economic impact.

Machine learning algorithms for predicting COVID-19 mortality in Ethiopia

BACKGROUND

Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.

METHODS

Our study involved analyzing electronic medical records of COVID-19 patients who were admitted to public hospitals in Ethiopia. Specifically, we developed seven different machine learning models to predict COVID-19 patient mortality. These models included J48 decision tree, random forest (RF), k-nearest neighborhood (k-NN), multi-layer perceptron (MLP), Naïve Bayes (NB), eXtreme gradient boosting (XGBoost), and logistic regression (LR). We then compared the performance of these models using data from a cohort of 696 patients through statistical analysis. To evaluate the effectiveness of the models, we utilized metrics derived from the confusion matrix such as sensitivity, specificity, precision, and receiver operating characteristic (ROC).

RESULTS

The study included a total of 696 patients, with a higher number of females (440 patients, accounting for 63.2%) compared to males. The median age of the participants was 35.0 years old, with an interquartile range of 18-79. After conducting different feature selection procedures, 23 features were examined, and identified as predictors of mortality, and it was determined that gender, Intensive care unit (ICU) admission, and alcohol drinking/addiction were the top three predictors of COVID-19 mortality. On the other hand, loss of smell, loss of taste, and hypertension were identified as the three lowest predictors of COVID-19 mortality. The experimental results revealed that the k-nearest neighbor (k-NN) algorithm outperformed than other machine learning algorithms, achieving an accuracy of 95.25%, sensitivity of 95.30%, precision of 92.7%, specificity of 93.30%, F1 score 93.98% and a receiver operating characteristic (ROC) score of 96.90%. These findings highlight the effectiveness of the k-NN algorithm in predicting COVID-19 outcomes based on the selected features.

CONCLUSION

Our study has developed an innovative model that utilizes hospital data to accurately predict the mortality risk of COVID-19 patients. The main objective of this model is to prioritize early treatment for high-risk patients and optimize strained healthcare systems during the ongoing pandemic. By integrating machine learning with comprehensive hospital databases, our model effectively classifies patients' mortality risk, enabling targeted medical interventions and improved resource management. Among the various methods tested, the K-nearest neighbors (KNN) algorithm demonstrated the highest accuracy, allowing for early identification of high-risk patients. Through KNN feature identification, we identified 23 predictors that significantly contribute to predicting COVID-19 mortality. The top five predictors are gender (female), intensive care unit (ICU) admission, alcohol drinking, smoking, and symptoms of headache and chills. This advancement holds great promise in enhancing healthcare outcomes and decision-making during the pandemic. By providing services and prioritizing patients based on the identified predictors, healthcare facilities and providers can improve the chances of survival for individuals. This model provides valuable insights that can guide healthcare professionals in allocating resources and delivering appropriate care to those at highest risk.

Incidence and predictors of acute kidney injury among adults admitted to the medical intensive care unit of a Comprehensive Specialized Hospital in Central Ethiopia

BACKGROUND

Acute kidney injury is a prevalent complication in the Intensive Care Unit (ICU) and a significant global public health concern. It affects approximately 13 million individuals and contributes to nearly two million deaths worldwide. Acute kidney injury among Intensive Care Unit patients is closely associated with higher rates of morbidity and mortality. This study aims to assess the incidence of acute kidney injury and identify predictors among adult patients admitted to the medical Intensive Care Unit.

METHOD

A retrospective follow-up study was conducted by reviewing charts of 317 systematically selected patients admitted to the Intensive Care Unit from September 1, 2018, to August 30, 2022, in Wachemo University Nigist Ellen Mohammed Memorial Comprehensive Specialized Hospital. The extraction tool was used for the data collection, Epi-data version 4.6.0 for data entry, and STATA version 14 for data cleaning and analysis. The Kaplan-Meier, log-rank test, and life table were used to describe the data. The Cox proportional hazard regression model was used for analysis.

RESULTS

Among the total study participants, 128 (40.4%) developed Acute Kidney Injury (AKI). The incidence rate of Acute Kidney Injury was 30.1 (95% CI: 25.33, 35.8) per 1000 person-days of observation, with a median survival time of 23 days. It was found that patients with invasive mechanical ventilation (AHR = 2.64; 95% CI: 1.46-4.78), negative fluid balance (AHR = 2.00; 95% CI: 1.30-3.03), hypertension (AHR = 1.6; 95% CI: 1.05-2.38), and a vasopressor (AHR = 1.72; 95% CI: 1.10-2.63) were independent predictors of acute kidney injury.

CONCLUSION

The incidence of Acute Kidney Injury was a major concern in the ICU of the study area. In the intensive care unit (ICU), it was found that patients with vasopressors, invasive mechanical ventilation, negative fluid balance, and chronic hypertension were independent predictors of developing AKI. It would be better if clinicians in the ICU provided targeted interventions through close monitoring and evaluation of those patients with invasive ventilation, chronic hypertension, negative fluid balance, and vasopressors.

Incidence and Predictors of Cardiac Arrest Among Patients Admitted to the Intensive Care Units of a Comprehensive Specialized Hospital in Central Ethiopia

BACKGROUND

Cardiac arrest (CA) is a common public health problem. Worldwide, cardiac arrest ranks highly among hospitalised patients' public health concerns, particularly in low-income nations. Data on cardiac arrest in intensive care units in low-income countries are relatively scarce. Determining the incidence and predictors of cardiac arrest among ICU patients will be a very crucial and fruitful clinical practice in resource-limited areas like Ethiopia.

METHODS

A retrospective cohort study was conducted by reviewing charts of 422 systematically selected patients admitted to the ICU from 2018 to 2022 in Wachemo University Comprehensive Specialized Hospital. The extraction tool was used for the data collection, Epi-data version 4.6.0 for data entry, and STATA version 14 for data cleaning and analysis. Kaplan-Meier, log rank test, and life table were used to describe the data. The Cox proportional hazard regression model was used for analysis.

RESULTS

The findings of this study revealed that the overall occurrence of cardiac arrest among critically ill ICU patients was 27% (95% CI: 23, 32). The incidence density rate of cardiac arrest among intensive care unit patients was 19.6 per 1000 person-days of observation. In a multivariable analysis, patients with chronic kidney disease, oxygen saturation <90%, delirium, intubation, and patients admitted to the ICU with cardiovascular disease were found to be independent predictors of cardiac arrest in the Intensive Care Unit.

CONCLUSION

The incidence density rate of cardiac arrest among intensive care unit patients was high. This study also revealed that chronic kidney disease, delirium, intubation, oxygen saturation level below 90% and patients admitted with cardiovascular disease were independent predictors of the occurrence of cardiac arrest among intensive care unit patients. Finally, we recommend that clinician pays attention to those identified as preventable risk factors for early interventions to improve the recovery process of patients in the ICU.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BACKGROUND

With the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain.

OBJECTIVE

In this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making.

METHODS

Specifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk.

RESULT

The proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes.

CONCLUSION

The experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.