A new COVID-19 prediction scoring model for in-hospital mortality: experiences from Turkey, single center retrospective cohort analysis

Combined systemic inflammatory indexes as reflectors of outcome in patients with COVID‑19 infection admitted to ICU

INTRODUCTION

The principal etiology of mortality in COVID-19 patients is the systemic pro-inflammatory processes which may lead to acute respiratory distress syndrome. Hematologic indices are reachable representatives of inflammation in patients with COVID-19 infection. The purpose of the current study was to evaluate the potential predictive value of these inflammatory indices in the in-hospital mortality of ICU-admitted COVID-19 patients. The studied indexes included AISI, dNLR, NLPR, NLR, SII, and SIRI.

METHOD

315 COVID-19 patients admitted to ICU managed in Imam Khomeini Hospital of Urmia, Iran, during the last 6 months of 2020 were retrospectively enrolled in the study and divided into two subgroups based on their final outcome, discharge or death.

RESULTS

Total leucocyte count (TLC), absolute neutrophil count (NLC), urea, Cr, RDW, AISI, dNLR, NLPR, NLR, SII, and SIRI were drastically elevated in the dead patients (P < 0.05). The optimal cut-off points for AISI (378.81), dNLR (5.66), NLPR (0.03), NLR (5.97), SII (1589.25), and SIRI (2.31) were obtained using ROC curves. NLR and SII had the highest sensitivity (71.4%) and specificity (73.6%), respectively. Patients with above-cut-off levels of ISI, dNLR, NLPR, NLR, and SII had lower average survival time. Age (OR = 1.057, CI95%: 1.030-1.085, p < 0.001) and dNLR (OR = 1.131, CI95%: 1.061-1.206, p < 0.001) were the independent predictors for mortality in the studied COVID-19 patients based on multivariate logistic regression.

CONCLUSION

Age and dNLR are valuable predictive factors for in-hospital death of ICU-admitted COVID-19 patients. Besides, other indices, AISI, NLPR, NLR, SII, and SIRI, may have an additional role that requires further investigation.

Comparison of Hematological Parameters Between Survivors and Non-Survivors COVID-19 Patients in Saudi Arabia

Objective

Coronavirus disease 2019, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a communicable disease transmitted through the respiratory route and bodily contact. The severity of infection and mortality rate of COVID-19 cases was significantly high in the initial stages of the pandemic. This study aims to investigate the hematological profile of COVID-19 survivors and non-survivors.

Methods

This is a single-center retrospective study. A total of 108 hospitalized patients with laboratory-confirmed COVID-19 at East Jeddah Hospital between April and August 2020 were categorized into two groups based on outcome as survivors (n = 54) and non-survivors (n = 54). Hematological parameters and clinical profiles were analyzed and compared between the two groups.

Results

The mean age and standard deviation of the survived (30-71 years) and non-survived (33-83) groups was 53 ± 10.8 and 57.9 ± 12.2 years, respectively, with no statistically significant difference in age between groups (p = 0.0513). Non-survivors had a significantly longer median length of stay in the intensive care unit (ICU) (7 days, IQR: 4.24 to 12) compared to survivors COVID-19 patients (5 days, IQR: 0 to 11.75) (p = 0.0151). For the survivors group, the participant's age positively correlated with the length of hospital stay (r(52) = 0.21, p = 0.0005) and ICU length of stay r(52) = 0.18, p = 0.001). The median red blood cells (RBC) counts were significantly higher in the survived group (4.56x109/L, IQR: 4.02 to 5.11) in comparison with the non-survived (4.23x109/L, IQR: 3.75 to 4.23) group (p = 0.0011). All COVID-19 patients exhibited lymphocytopenia and a significant negative correlation was observed between the lymphocyte values and length of hospital stay among the survived group (p < 0.001) as well as length of ICU stay among the survived group (p < 0.0480). Disease-related mortality was significantly associated with reduced white blood cells (WBCs) (8.5×109/L, IQR: 6.1 to 11.7) and reduced basophils (0.09%, IQR: 0.02 to 0.19). Additionally, statistically significant differences were found between the survived and non-survived groups with respect to prothrombin time (PT) (12.5 sec. vs 14 sec., p < 0.0001) and partial thromboplastin time (PTT) (31.8 sec. vs 40 sec., p = 0.0008).

Conclusion

Hematological parameters can serve as valuable indicators to identify patients with severe COVID-19 and expected poor-prognosis/outcomes upon hospital admission. Cell counts of lymphocytes, WBCs, basophils and parameters such as PT and PTT can serve as clinical indicators to assess disease severity and predict progression to critical illness.

High Systemic Immune-Inflammation Index, Predicting Early Allograft Dysfunction, Indicates High 90-Day Mortality for Acute-On-Chronic Liver Failure after Liver Transplantation

INTRODUCTION

The aim of the study was to investigate the relationship between systemic immune-inflammation index (SII) and early allograft dysfunction (EAD) and 90-day mortality after liver transplantation (LT) in acute-on-chronic liver failure (ACLF).

METHODS

Retrospective record analysis was done on 114 patients who had LT for ACLF. To identify the ideal SII, the receiver operating characteristic curve was used. The incidence of EAD and 90-day mortality following LT were calculated. The prognostic value of SII was assessed using the Kaplan-Meier technique and the Cox proportional hazards model.

RESULTS

The cut-off for SII was 201.5 (AUC = 0.728, p < 0.001). EAD occurred in 40 (35.1%) patients of the high SII group and 5 (4.4%) patients of the normal SII group, p < 0.001. 18 (15.8%) deaths occurred in the high SII group and 2 (1.8%) deaths occurred in the normal SII group, p = 0.008. The multivariate analysis demonstrated that SII ≥201.5, MELD ≥27 were independent prognostic factors for 90-day mortality after LT.

CONCLUSION

SII predicts the occurrence of EAD and is an independent risk factor for 90-day mortality after LT.

A novel risk classifier to predict the in-hospital death risk of nosocomial infections in elderly cancer patients

Background

Elderly cancer patients are more predisposed to developing nosocomial infections during anti-neoplastic treatment, and are associated with a bleaker prognosis. This study aimed to develop a novel risk classifier to predict the in-hospital death risk of nosocomial infections in this population.

Methods

Retrospective clinical data were collected from a National Cancer Regional Center in Northwest China. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was utilized to filter the optimal variables for model development and avoid model overfitting. Logistic regression analysis was performed to identify the independent predictors of the in-hospital death risk. A nomogram was then developed to predict the in-hospital death risk of each participant. The performance of the nomogram was evaluated using receiver operating characteristics (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

A total of 569 elderly cancer patients were included in this study, and the estimated in-hospital mortality rate was 13.9%. The results of multivariate logistic regression analysis showed that ECOG-PS (odds ratio [OR]: 4.41, 95% confidence interval [CI]: 1.95-9.99), surgery type (OR: 0.18, 95%CI: 0.04-0.85), septic shock (OR: 5.92, 95%CI: 2.43-14.44), length of antibiotics treatment (OR: 0.21, 95%CI: 0.09-0.50), and prognostic nutritional index (PNI) (OR: 0.14, 95%CI: 0.06-0.33) were independent predictors of the in-hospital death risk of nosocomial infections in elderly cancer patients. A nomogram was then constructed to achieve personalized in-hospital death risk prediction. ROC curves yield excellent discrimination ability in the training (area under the curve [AUC]=0.882) and validation (AUC=0.825) cohorts. Additionally, the nomogram showed good calibration ability and net clinical benefit in both cohorts.

Conclusion

Nosocomial infections are a common and potentially fatal complication in elderly cancer patients. Clinical characteristics and infection types can vary among different age groups. The risk classifier developed in this study could accurately predict the in-hospital death risk for these patients, providing an important tool for personalized risk assessment and clinical decision-making.

Evaluation of Inflammation-Based Prognostic Risk Scores in Predicting in-Hospital Mortality Risk in COVID-19 Patients: A Cross-Sectional Retrospective Study

Objective

Systemic inflammatory parameters are predictors of poor prognosis in COVID-19 patients. This study evaluated whether the prognostic nutritional index, which was also related to nutrition risk and other inflammation-based prognostic scores, was predictive of in-hospital mortality in COVID-19 patients.

Materials and Methods

This was a retrospective cross-sectional single-center study. Based on the exclusion criteria, 151 patients over 18 years old diagnosed with COVID-19 and hospitalized in the intensive care unit between March 2020 and December 2020 were eligible for this study. Multivariable logistic regression analysis was performed to evaluate the predictive value of the Glasgow Prognostic Score (GPS), Prognostic Index (PI), Prognostic Nutritional Index (PNI), and Systemic Inflammatory Index (SII).

Results

In the univariate analyses, age, diabetes mellitus (DM), chronic kidney disease, acute kidney injury, hypothyroidism, hospitalization stay, lactate dehydrogenase (LDH), aspartate aminotransferase (AST), D-dimer, ferritin, C-reactive protein (CRP), albumin, hemoglobin level, platelet count, urea, creatinine level, PNI, GPS were significantly associated with mortality. However, in the multivariable logistic regression analysis of the inflammation-based prognostic scores, only PNI was statistically significant in predicting in-hospital mortality (OR=0.83; [95% CI=0.71-0.97]; p =0.019).

Conclusion

PNI is a more useful and powerful tool among these inflammation-based prognostic risk scores in predicting in-hospital mortality in COVID-19 patients.

Prognostic Nutritional Index and Oxygen Therapy Requirement Associated With Longer Hospital Length of Stay in Patients With Moderate to Severe COVID-19: Multicenter Prospective Cohort Analyses

Purpose

To evaluate whether the prognostic nutritional index (PNI) is related to the oxygen therapy requirement at hospital admission and to ascertain the prognostic effect of the PNI and the oxygen therapy requirement as predictors of hospital length of stay in patients with moderate to severe coronavirus disease 2019 (COVID-19).

Methods

This is a post-hoc analysis in hospitalized patients with moderate to severe COVID-19. The participants were categorized: (1) non-oxygen therapy (moderate COVID-19 not requiring oxygen therapy); (2) nasal cannula therapy (severe COVID-19 requiring nasal cannula oxygen therapy); and (3) high-flow therapy (severe COVID-19 requiring high-flow oxygen therapy). PNI was calculated for each patient according to the following equation: serum albumin [g/dL] × 10 + total lymphocyte count [per mm³] × 0.005. The participants were categorized into malnutrition (PNI <40), mild malnutrition (PNI 40-45), and non-malnutrition (PNI > 45).

Results

According to PNI, malnutrition was more prevalent in the high-flow therapy group (94.9%; P < 0.001) with significantly lower PNI compared to both groups even after adjusting for the center and C-reactive protein. Patients in the high-flow therapy group [9 days (95% CI 7.2, 10.7), P < 0.001] and malnutrition status [7 days (95% CI 6.6, 7.4), P = 0.016] showed a significant longer hospital length of stay compared to their counterparts. The multivariable Cox proportional hazard models showed significant associations between both oxygen therapy requirement and PNI categories and hospital discharge.

Conclusion

In addition to oxygen therapy requirement, low PNI was associated with longer hospital length of stay. Our findings suggest that PNI could be useful in the assessment of nutritional status related to the prognosis of patients with moderate to severe COVID-19.

Are gastrointestinal symptoms associated with higher risk of Mortality in COVID-19 patients? A systematic review and meta-analysis

BACKGROUND

Gastrointestinal symptoms have been reported in patients with COVID-19. Several clinical investigations suggested that gastrointestinal symptoms were associated with disease severity of COVID-19. However, the relevance of gastrointestinal symptoms and mortality of COVID-19 remains largely unknown. We aim to investigate the relationship between gastrointestinal symptoms and COVID-19 mortality.

METHODS

We searched the PubMed, Embase, Web of science and Cochrane for studies published between Dec 1, 2019 and May 1, 2021, that had data on gastrointestinal symptoms in COVID-19 patients. Additional literatures were obtained by screening the citations of included studies and recent reviews. Only studies that reported the mortality of COVID-19 patients with/without gastrointestinal symptoms were included. Raw data were pooled to calculate OR (Odds Ratio). The mortality was compared between patients with and without gastrointestinal symptoms, as well as between patients with and without individual symptoms (diarrhea, nausea/vomiting, abdominal pain).

RESULTS

Fifty-three literatures with 55,245 COVID-19 patients (4955 non-survivors and 50,290 survivors) were included. The presence of GI symptoms was not associated with the mortality of COVID-19 patients (OR=0.88; 95% CI 0.71-1.09; P=0.23). As for individual symptoms, diarrhea (OR=1.01; 95% CI 0.72-1.41; P=0.96), nausea/vomiting (OR=1.16; 95% CI 0.78-1.71; P=0.46) and abdominal pain (OR=1.55; 95% CI 0.68-3.54; P=0.3) also showed non-relevance with the death of COVID-19 patients.

CONCLUSIONS

Gastrointestinal symptoms are not associated with higher mortality of COVID-19 patients. The prognostic value of gastrointestinal symptoms in COVID-19 requires further investigation.

The Predictive Role of NLR, d-NLR, MLR, and SIRI in COVID-19 Mortality

(1) Background: Since its discovery, COVID-19 has caused more than 256 million cases, with a cumulative death toll of more than 5.1 million, worldwide. Early identification of patients at high risk of mortality is of great importance in saving the lives of COVID-19 patients. The study aims to assess the utility of various inflammatory markers in predicting mortality among hospitalized patients with COVID-19. (2) Methods: A retrospective observational study was conducted among 108 patients with laboratory-confirmed COVID-19 hospitalized between 1 May 2021 and 31 October 2021 at Municipal Emergency Clinical Hospital of Timisoara, Romania. Blood cell counts at admission were used to obtain NLR, dNLR, MLR, PLR, SII, and SIRI. The association of inflammatory index and mortality was assessed via Kaplan–Maier curves univariate Cox regression and binominal logistic regression. (3) Results: The median age was 63.31 ± 14.83, the rate of in-hospital death being 15.7%. The optimal cutoff for NLR, dNLR, MLR, and SIRI was 9.1, 9.6, 0.69, and 2.2. AUC for PLR and SII had no statistically significant discriminatory value. The binary logistic regression identified elevated NLR (aOR = 4.14), dNLR (aOR = 14.09), and MLR (aOR = 3.29), as independent factors for poor clinical outcome of COVID-19. (4) Conclusions: NLR, dNLR, MLR have significant predictive value in COVID-19 mortality.

Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis

Coronavirus disease 2019 (COVID-19) has resulted in a global pandemic, challenging both the medical and scientific community for the development of novel vaccines and a greater understanding of the effects of the SARS-CoV-2 virus. COVID-19 has been associated with a pronounced and out-of-control inflammatory response. Studies have sought to understand the effects of inflammatory response markers to prognosticate the disease. Herein, we aimed to review the evidence of 11 groups of systemic inflammatory markers for risk-stratifying patients and prognosticating outcomes related to COVID-19. Numerous studies have demonstrated the effectiveness of neutrophil to lymphocyte ratio (NLR) in prognosticating patient outcomes, including but not limited to severe disease, hospitalization, intensive care unit (ICU) admission, intubation, and death. A few markers outperformed NLR in predicting outcomes, including 1) systemic immune-inflammation index (SII), 2) prognostic nutritional index (PNI), 3) C-reactive protein (CRP) to albumin ratio (CAR) and high-sensitivity CAR (hsCAR), and 4) CRP to prealbumin ratio (CPAR) and high-sensitivity CPAR (hsCPAR). However, there are a limited number of studies comparing NLR with these markers, and such conclusions require larger validation studies. Overall, the evidence suggests that most of the studied markers are able to predict COVID-19 prognosis, however NLR seems to be the most robust marker.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE

To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease.

DESIGN

Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group.

DATA SOURCES

PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020.

STUDY SELECTION

Studies that developed or validated a multivariable covid-19 related prediction model.

DATA EXTRACTION

At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool).

RESULTS

37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models.

CONCLUSION

Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline.

SYSTEMATIC REVIEW REGISTRATION

Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

READERS' NOTE

This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.