Development of a scoring system for the prediction of in-hospital mortality among COVID-19 patients

A clinical prediction model for predicting the surgical site infection after an open reduction and internal fixation procedure considering the NHSN/SIR risk model: a multicenter case-control study

Introduction

Surgical site infection (SSI) is one of the most common surgical-related complications worldwide, particularly in developing countries. SSI is responsible for mortality, long hospitalization period, and a high economic burden.

Method

This hospital-based case-control study was conducted in six educational hospitals in Tehran, Iran. A total of 244 patients at the age of 18-85 years who had undergone open reduction and internal fixation (ORIF) surgery were included in this study. Among the 244 patients, 122 patients who developed SSIs were selected to be compared with 122 non-infected patients used as controls. At the second stage, all patients (n = 350) who underwent ORIF surgery in a hospital were selected for an estimation of the standardized infection ratio (SIR). A logistic regression model was used for predicting the most important factors associated with the occurrence of SSIs. Finally, the performance of the ORIF prediction model was evaluated using discrimination and calibration indices. Data were analyzed using R.3.6.2 and STATA.14 software.

Results

Klebsiella (14.75%) was the most frequently detected bacterium in SSIs following ORIF surgery. The results revealed that the most important factors associated with SSI following an ORIF procedure were found to be elder age, elective surgery, prolonged operation time, American Society of Anesthesiologists score of ≥2, class 3 and 4 wound, and preoperative blood glucose levels of >200 mg/dl; while preoperative higher hemoglobin level (g/dl) was found to be a protective factor. The evidence for the interaction effect between age and gender, body mass index and gender, and age and elective surgery were also observed. After assessing the internal validity of the model, the overall performance of the models was found to be good with an area under the curve of 95%. The SIR of SSI for ORIF surgery in the selected hospital was 0.66 among the patients aged 18-85 years old.

Conclusion

New risk prediction models can help in detecting high-risk patients and monitoring the infection rate in hospitals based on their infection prevention and control programs. Physicians using prediction models can identify high-risk patients with these factors prior to ORIF procedure.

Risk Factors Associated with Severity and Death from COVID-19 in Iran: A Systematic Review and Meta-Analysis Study

Objectives: This study aims to investigate the risk factors associated with severity and death from COVID-19 through a systematic review and meta-analysis of the published documents in Iran. Methods: A systematic search was performed based on all articles indexed in Scopus, Embase, Web of Science (WOS), PubMed, and Google Scholar in English and Scientific Information Database (SID) and Iranian Research Institute for Information Science and Technology (IRA)NDOC indexes in Persian. To assess quality, we used the Newcastle Ottawa Scale. Publication bias was assessed using Egger's tests. Forest plots were used for a graphical description of the results. We used HRs, and ORs reported for the association between risk factors and COVID-19 severity and death. Results: Sixty-nine studies were included in the meta-analysis, of which 62 and 13 had assessed risk factors for death and severity, respectively. The results showed a significant association between death from COVID-19 and age, male gender, diabetes, hypertension, cardiovascular disease (CVD), cerebrovascular disease, chronic kidney disease (CKD), Headache, and Dyspnea. We observed a significant association between increased white blood cell (WBC), decreased Lymphocyte, increased blood urea nitrogen (BUN), increased creatinine, vitamin D deficiency, and death from COVID-19. There was only a significant relationship between CVD and disease severity. Conclusion: It is recommended that the predictive risk factors of COVID-19 severity and death mentioned in this study to be used for therapeutic and health interventions, to update clinical guidelines and determine patients' prognoses.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Development and validation of a predictive scoring system for in-hospital mortality in COVID-19 Egyptian patients: a retrospective study

SARS-CoV-2 virus has rapidly spread worldwide since December 2019, causing COVID-19 disease. In-hospital mortality is a common indicator for evaluating treatment outcomes. Therefore, the developing and validating a simple score system from observational data could assist in modulating the management procedures. A retrospective cohort study included all data records of patients with positive PCR for SARS-CoV-2. The factors that associated with mortality were analyzed, then allocation of potential predictors of mortality was executed using different logistic regression modeling, subsequently scoring system was developed from the most weighted predictors. The mortality rate of patients with COVID-19 pneumonia was 28.5% and 28.74%, respectively. The most significant factors that affected in-hospital mortality were old age (> 60 years), delay in hospital admission (> 4 days), high neutrophil/lymphocyte ratio "NLR" (> 3); higher computed tomography severity score; and CT-SS (> 20), in addition to using remdesivir and tocilizumab in the treatment protocol (P < 0.001 for all). The validity of the newly performed score was significant; the AUC was 85%, P < 0.001, and its prognostic utility was good; the AUC was 75%, P < 0.001. The prognostic utility of newly developed score system (EGY.Score) was excellent and could be used to adjust the treatment strategy of highly at-risk patients with COVID-19 pneumonia.

Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis

Aim

To develop an accurate lab score based on in-hospital patients’ potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission.

Methods

To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer’s test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022.

Results

Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883–0.945, 95% CI) and 0.873 (0.778–0.969, 95% CI) respectively.

Conclusion

The development of proper lab scores, based on patients’ clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients’ survivability.

Novel Scoring Systems to Predict the Need for Oxygenation and ICU Care, and Mortality in Hospitalized COVID-19 Patients: A Risk Stratification Tool

Introduction: A rapid surge in cases during the COVID-19 pandemic can overwhelm any healthcare system. It is imperative to triage patients who would require oxygen and ICU care, and predict mortality. Specific parameters at admission may help in identifying them.

Methodology: A prospective observational study was undertaken in a COVID-19 ward of a tertiary care center. All baseline clinical and laboratory data were captured. Patients were followed till death or discharge. Univariable and multivariable logistic regression was used to find predictors of the need for oxygen, need for ICU care, and mortality. Objective scoring systems were developed for the same using the predictors.

Results: The study included 209 patients. Disease severity was mild, moderate, and severe in 98 (46.9%), 74 (35.4%), and 37 (17.7%) patients, respectively. The neutrophil-to-lymphocyte ratio (NLR) >4 was a common independent predictor of the need for oxygen (p<0.001), need for ICU transfer (p=0.04), and mortality (p=0.06). Clinical risk scores were developed (10*c-reactive protein (CRP) + 14.8*NLR + 12*urea), (10*aspartate transaminase (AST) + 15.7*NLR + 14.28*CRP), (10*NLR + 10.1*creatinine) which, if ≥14.8, ≥25.7, ≥10.1 predicted need for oxygenation, need for ICU transfer and mortality with a sensitivity and specificity (81.6%, 70%), (73.3%, 75.7%), (61.1%, 75%), respectively. 

Conclusion: The NLR, CRP, urea, creatinine, and AST are independent predictors in identifying patients with poor outcomes. An objective scoring system can be used at the bedside for appropriate triaging of patients and utilization of resources.