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Dec 2021 DOI 10.14302/issn.2692-1537.ijcv-21-4025
M. Quirit AllenCorresponding author
MD.
Introduction The COVID-19 pandemic continues to affect a large swath of the global population. The Philippine records four hundred seventy-four thousand sixty-four (474, 064) confirmed COVID 19 cases since December 31 2020. The COVID 19 pandemic recently highlighted the role of systemic hyperferritenemia as a major cause of death. In this study, we were able to correlate the serum ferritin level and predict 30 day in hospital mortality in COVID 19 pneumonia. Objective The aim of the study is to investigate the correlation between serum ferritin level and disease mortality in COVID19 pneumonia with subset analysis on demographics and co-morbidities of patients with COVID 19 pneumonia. Methodology We reviewed the records of all laboratory confirmed COVID 19 patients from World Citi Medical Center from April 2020 up to April 2021.A statistically significant sample size of seventy nine (79) admitted patients were used in this study. A serum ferritin level was assayed using electrochemilumenescence immunoassay with a Roche COBAS analyzer. Results Result showed that high ferritin level is associated with in hospital mortality. With ferritin level of 1437.07ng/ml, poor clinical outcome and in hospital mortality was considered. We also observed that demographics and co morbidities of patients in this study were significant to predict in hospital mortality. Further sub-analysis of co morbidities such as Hypertensive cardiovascular disease, Type 2 Diabetes Mellitus, Chronic kidney disease, Liver disease, Chronic obstructive pulmonary disease and Cerebrovascular disease showed poor outcome which were directly related to ferritin levels with p value of <0.0001. Conclusion This study has demonstrated that elevated ferritin levels were shown to correlate with 30 day in hospital mortality as well as medical comorbidities such as Hypertensive Cardiovascular disease, Type 2 Diabetes Mellitus, and chronic kidney disease have shown significant evidence for in hospital mortality.
Jun 2026 DOI 10.14302/issn.2642-9241.jrd-26-6332
de Melo PhilipCorresponding author
Respiratory diseases remain a major contributor to hospital morbidity and mortality worldwide, particularly among elderly patients and individuals with severe pulmonary compromise. Accurate prediction of respiratory mortality is clinically important for triage, resource allocation, ICU utilization, and early intervention. Traditional statistical models frequently demonstrate limited predictive sensitivity because respiratory mortality is influenced by complex interactions among demographic, diagnostic, physiologic, and severity-related variables. In this study, a machine learning framework was developed to predict in-hospital mortality among patients with respiratory disease using administrative and clinically derived variables, including age, sex, length of stay (LOS), diagnostic descriptions, risk of mortality and severity scores. A Random Forest classifier with balanced class weighting was developed and implemented to address nonlinear relationships and class imbalance within the dataset. Initial modeling demonstrated good overall discrimination performance, with receiver operating characteristic area under the curve (ROC-AUC) values approaching 0.84; however, mortality recall remained limited because deceased patients represented a minority class within the original dataset. To improve mortality detection, a physiologically informed synthetic augmentation strategy was developed. Synthetic clinical variables included oxygen saturation, ICU status, ventilator support, sepsis status, systolic blood pressure, creatinine, and lactate levels. Conditional physiologic consistency rules were incorporated during augmentation to preserve clinically plausible relationships among respiratory failure, hemodynamic instability, and organ dysfunction. The augmented dataset substantially improved model sensitivity and balanced mortality classification performance. Final model evaluation demonstrated strong predictive capability, achieving approximately 97% classification accuracy with balanced precision and recall across mortality classes. Confusion matrix analysis revealed marked reduction in false-negative mortality predictions compared with baseline modeling approaches. Feature importance analysis identified physiologic instability markers, respiratory severity classifications, LOS, and diagnostic respiratory categories as dominant predictors of mortality. These findings suggest that hybrid simulation-augmented machine learning frameworks may provide a valuable strategy for respiratory mortality analytics, particularly in datasets with limited real-world mortality prevalence and incomplete physiologic representation.
Dec 2021 DOI 10.14302/issn.2692-1537.ijcv-21-4045
D. Natividad III GracianoCorresponding author
Philippines.
Introduction In December 2019, cases of serious illness causing pneumonia and death were first reported in Wuhan, China.2 The clinical features of Corona Virus Disease-19 (COVID-19) are ranging from asymptomatic to multi organ dysfunction. The disease can progress to pneumonia, respiratory failure and death.4 Thus, a tool is needed that can predict the severity and in-hospital mortality risk of a patient with COVID-19 Pneumonia. The PIRO (predisposition, insult, response, and organ dysfunction) scoring was developed for use in the emergency department to risk stratify sepsis cases.15 Eventually it was adapted in pneumonia cases to predict its severity. Objective To validate PIRO score as an assessment tool for COVID-19 mortality risk among patients with confirmed COVID-19 RT-PCR test among patients aged 19 and above admitted in World Citi Medical Center from March 2020 to August 2020 Methods This study included 93 patients aged 19 and above admitted in World Citi Medical Center with a primary diagnosis of COVID-19 Confirmed with pneumonia between March 2020 to August 2020. The patients’ charts were retrieved from the hospital medical records and case notes were reviewed. A severity assessment score was developed based on PIRO score (Predisposition comorbidities and age; Insult multilobar opacities and viremia; Response shock and hypoxemia; Organ Dysfunciton) were extracted. The patients were stratified in four levels of risk: a)Low,0-2 points; b)Mild,3 points; c)High,4 points; d)Very High,5-8 points. The PIRO score and the clinical outcome were compared. The discriminative ability of PIRO score to predict mortality risk was evaluated under receiver operating characteristic curve (AUC). Results The PIRO score had an excellent predictive ability for in-hospital mortality (AUC0.9197). Analysis of variance showed that higher levels of PIRO scores were significantly associated with higher mortality (p<0.001). Patients with Mild PIRO risk category were 98.65% less likely to expire (p<0.001, 95%CI 0.0015) and High PIRO risk category were 94.47% less likely to expire (p<0.001, 95%CI 0.0124), both compared to patients with Very high PIRO risk category. Finally, Very High PIRO risk category were more than 44 times likely to expire compared to patients with Low, Mild and High PIRO risk category (p<0.001, 95%CI 11.738). Conclusions The PIRO score is a valid risk model that can be used to predict in-hospital mortality, that can help clinicians provide timely and accurate assessment, and hence appropriate management to patients with COVID-19 Pneumonia.
Jan 2013 DOI 10.14302/issn.2329-9487.jhc-12-63
Wunderlich CarstenCorresponding author
University of Technology Dresden, Department of Cardiology, Medical Clinic, Fetscherstr. 76, Dresden 01307, Germany.
Introduction: Elevated serum troponin levels are a reliable indicator of right ventricular wall stress in patients with acute pulmonary artery embolism (PE). Raised troponin levels have been shown to predict adverse clinical outcome in these individuals. In this context it was our aim to determine the additional role of the heart-type fatty acid-binding protein (H-FABP) in patients with acute PE. Methods: 87 consecutive patients with confirmed PE were included in the present study. On arrival a qualitative H-FABP-test (positive cut-off 7ng/ml) and a detailed echocardiographic study were performed in all patients. These findings were related to both in hospital and 30 days mortality. Results: Of the included 87 patients, 17 had positive H-FABP-tests. Right ventricular function was severely deteriorated in 10 patients (59%) in the H-FABP-positive group but only in 2 patients (3%) of the H-FABP-negative group (p<0.005). 15/17 patients (88%) of the H-FABP-positive group needed inotropic support, of these, 14 (82%) died in hospital. Only one patient with a H-FABP-negative test (n= 70) needed vasoactive drugs, and none of these patients died (p<0.005). Conclusion: H-FABP showed a better correlation with in-hospital mortality and RV-function than troponin I. Our data show that H-FABP significantly correlates with in hospital and 30-day mortality in patients with PE. Furthermore, it is associated with impaired right ventricular function and showed better correlation with mortality in patients with PE as compared to Troponin I. Thus, it may be viewed as a novel and promising tool to optimise the management strategy in these patients.