Development of a risk score to identify patients at high risk for a severe course of COVID-19.

Aim: We aimed to develop a risk score to calculate a person's individual risk for a severe COVID-19 course (POINTED score) to support prioritization of especially vulnerable patients for a (booster) vaccination. Subject and methods: This cohort study was based on German claims data and included 623,363 individuals with a COVID-19 diagnosis in 2020. The outcome was COVID-19 related treatment in an intensive care unit, mechanical ventilation, or death after a COVID-19 infection. Data were split into a training and a test sample. Poisson regression models with robust standard errors including 35 predefined risk factors were calculated. Coefficients were rescaled with a min-max normalization to derive numeric score values between 0 and 20 for each risk factor. The scores' discriminatory ability was evaluated by calculating the area under the curve (AUC). Results: Besides age, down syndrome and hematologic cancer with therapy, immunosuppressive therapy, and other neurological conditions were the risk factors with the highest risk for a severe COVID-19 course. The AUC of the POINTED score was 0.889, indicating very good predictive validity. Conclusion: The POINTED score is a valid tool to calculate a person's risk for a severe COVID-19 course. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01884-7.

Clinical Characteristics and Risk Prediction Score in Patients With Mild-to-Moderate Coronavirus Disease 2019 in Japan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, causing widespread mortality. Many patients with COVID-19 have been treated in homes, hotels, and medium-sized hospitals where doctors were responsible for assessing the need for critical care hospitalization. This study aimed to establish a severity prediction score for critical care triage. METHOD: We analyzed the data of 368 patients with mild-to-moderate COVID-19 who had been admitted to Fussa Hospital, Japan, from April 2020 to February 2022. We defined a high-oxygen group as requiring ≥4 l/min of oxygen. Multivariable logistic regression was used to construct a risk prediction score, and the best model was selected using a stepwise selection method. RESULTS: Multivariable analysis showed that older age (≥70 years), elevated creatine kinase (≥127 U/L), C-reactive protein (≥2.19 mg/dL), and ferritin (≥632.7 ng/mL) levels were independent risk factors associated with the high-oxygen group. Each risk factor was assigned a score ranging from 0 to 4, and we referred to the final overall score as the Fussa score. Patients were classified into two groups, namely, high-risk (total risk factors, ≥2) and low-risk (total risk score, <2) groups. The high-risk group had a significantly worse prognosis (low-risk group, undefined vs. high-risk group, undefined; P< 0.0001). CONCLUSIONS: The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

Derivation and validation of a clinical decision rule to risk-stratify COVID-19 patients discharged from the emergency department: The CCEDRRN COVID discharge score.

Objective: To risk-stratify COVID-19 patients being considered for discharge from the emergency department (ED). Methods: We conducted an observational study to derive and validate a clinical decision rule to identify COVID-19 patients at risk for hospital admission or death within 72 hours of ED discharge. We used data from 49 sites in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) between March 1, 2020, and September 8, 2021. We randomly assigned hospitals to derivation or validation and prespecified clinical variables as candidate predictors. We used logistic regression to develop the score in a derivation cohort and examined its performance in predicting short-term adverse outcomes in a validation cohort. Results: Of 15,305 eligible patient visits, 535 (3.6%) experienced the outcome. The score included age, sex, pregnancy status, temperature, arrival mode, respiratory rate, and respiratory distress. The area under the curve was 0.70 (95% confidence interval [CI] 0.68-0.73) in derivation and 0.71 (95% CI 0.68-0.73) in combined derivation and validation cohorts. Among those with a score of 3 or less, the risk for the primary outcome was 1.9% or less, and the sensitivity of using 3 as a rule-out score was 89.3% (95% CI 82.7-94.0). Among those with a score of ≥9, the risk for the primary outcome was as high as 12.2% and the specificity of using 9 as a rule-in score was 95.6% (95% CI 94.9-96.2). Conclusion: The CCEDRRN COVID discharge score can identify patients at risk of short-term adverse outcomes after ED discharge with variables that are readily available on patient arrival.

Incidence and severity prediction score of COVID-19 in people living with HIV (SCOVHIV): experience from the first and second waves of the pandemic in Indonesia.

BACKGROUND: People living with HIV (PLHIV) have higher risk of COVID-19 infection and mortality due to COVID-19. Health professionals should be able to assess PLHIV who are more likely to develop severe COVID-19 and provide appropriate medical treatment. This study aimed to assess clinical factors associated with COVID-19 severity and developed a scoring system to predict severe COVID-19 infection among PLHIV. METHODS: This retrospective cohort study evaluated PLHIV at four hospitals diagnosed with COVID-19 during the first and second wave COVID-19 pandemic in Indonesia. The independent risk factors related to the severity of COVID-19 were identified with multivariate logistic regression. RESULTS: 342 PLHIV were diagnosed with COVID-19, including 23 with severe-critical diseases. The cumulative incidence up to December 2021 was 0.083 (95% CI 0.074-0.092). Twenty-three patients developed severe-critical COVID-19, and the mortality rate was 3.2% (95% CI 1.61%-5.76%). Having any comorbidity, CD4 count of < 200 cells/mm3, not being on ART, and active opportunistic infection were independent risk factors for developing severe COVID-19. SCOVHIV score was formulated to predict severity, with 1 point for each item. A minimum score of 3 indicated a 58.4% probability of progressing to severe COVID-19. This scoring system had a good discrimination ability with the area under the curve (AUC) of 0.856 (95% CI 0.775-0.936). CONCLUSION: SCOVHIV score, a four-point scoring system, had good accuracy in predicting COVID-19 severity in PLHIV.

Reliability of predictive models to support early decision making in the emergency department for patients with confirmed diagnosis of COVID-19: the Pescara Covid Hospital score.

BACKGROUND: The hospital management of patients diagnosed with COVID-19 can be hampered by heterogeneous characteristics at entry into the emergency department. We aimed to identify demographic, clinical and laboratory parameters associated with higher risks of hospitalisation, oxygen support, admission to intensive care and death, to build a risk score for clinical decision making at presentation to the emergency department. METHODS: We carried out a retrospective study using linked administrative data and laboratory parameters available in the initial phase of the pandemic at the emergency department of the regional reference hospital of Pescara, Abruzzo, Italy, March-June 2020. Logistic regression and Cox modelling were used to identify independent predictors for risk stratification. Validation was carried out collecting data from an extended timeframe covering other variants of concern, including Alpha (December 2020-January 2021) and Delta/Omicron (January-March 2022). RESULTS: Several clinical and laboratory parameters were significantly associated to the outcomes of interest, independently from age and gender. The strongest predictors were: for hospitalisation, monocyte distribution width ≥ 22 (4.09; 2.21-7.72) and diabetes (OR = 3.04; 1.09-9.84); for oxygen support: saturation < 95% (OR = 11.01; 3.75-41.14), lactate dehydrogenase≥237 U/L (OR = 5.93; 2.40-15.39) and lymphocytes< 1.2 × 103/µL (OR = 4.49; 1.84-11.53); for intensive care, end stage renal disease (OR = 59.42; 2.43-2230.60), lactate dehydrogenase≥334 U/L (OR = 5.59; 2.46-13.84), D-dimer≥2.37 mg/L (OR = 5.18; 1.14-26.36), monocyte distribution width ≥ 25 (OR = 3.32; 1.39-8.50); for death, procalcitonin≥0.2 ng/mL (HR = 2.86; 1.95-4.19) and saturation < 96% (HR = 2.74; 1.76-4.28). Risk scores derived from predictive models using optimal thresholds achieved values of the area under the curve between 81 and 91%. Validation of the scoring algorithm for the evolving virus achieved accuracy between 65 and 84%. CONCLUSIONS: A set of parameters that are normally available at emergency departments of any hospital can be used to stratify patients with COVID-19 at risk of severe conditions. The method shall be calibrated to support timely clinical decision during the first hours of admission with different variants of concern.

Discriminant Ability of In-Hospital Mortality Based on Prognostic Scores of Elderly Patients with COVID-19: A Taiwan Medical Center Study

Introduction: The coronavirus disease 2019 (COVID-19) has brought excessive patients in emergency departments. Several COVID-19 prediction scores have been developed to aid in the patient disposition of emergency physicians. This study aimed to validate different COVID-19 prediction scores. Method: ???DynaMed??? was used to retrieve high-quality COVID-19 prediction scores for the evaluation of in-hospital mortality rate. SEIMC score, 4C-Mortality score, SOARS score, and Veterans Health Administration COVID-19 (VACO) Index were selected. A retrospective, single-center study was done on elderly patients hospitalized for COVID-19 from May 2021 to July 2021 in MacKay Memorial Hospital. Patients who were (I) negative for COVID-19 examination, (II) aged 65 years old, (III) previously infected with COVID-19 and de-isolated (IV) hospital-acquired COVID-19 infection, (V) not admitted for hospitalization, and (VI) with missing of demographic characteristics were excluded. The area under the receiver operating characteristic curves (AUC) was computed to predict the in-hospital mortality rate. Result: Of 66,090 patients who underwent COVID-19 examination in MacKay Memorial Hospital, 133 patients were included in this study, with 26 deceased patients (19.5%). Among included patients, the median age was 74.38 years and 53% patients were male. Of the selected COVID-19 prediction scores, 4C-Mortality Score (AUC = 0.8), SEIMC score (AUC = 0.75), and SOARS score (AUC = 0.72) contained a good prognostic value, with an AUC 0.70. VACO index demonstrated less predictive value (AUC = 0.61). Conclusion: COVID-19 prediction scores were validated, and it was found that 4C-Mortality Score, SEIMC score, and SOARS score performed well in predicting the in-hospital mortality rate of elderly patients with COVID-19, and 4C-Mortality score is best appreciated.

Mortality Predictors in Severe SARS-CoV-2 Infection.

Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department.

BACKGROUND: Critically-ill Covid-19 patients require extensive resources which can overburden a healthcare system already under strain due to a pandemic. A good disease severity prediction score can help allocate resources to where they are needed most. OBJECTIVES: We developed a Covid-19 Severity Assessment Score (CoSAS) to predict those patients likely to suffer from mortalities within 28 days of hospital admission. We also compared this score to Quick Sequential Organ Failure Assessment (qSOFA) in adults. METHODS: CoSAS includes the following 10 components: Age, gender, Clinical Frailty Score, number of comorbidities, Ferritin level, D-dimer level, neutrophil/lymphocyte ratio, C-reactive Protein levels, systolic blood pressure and oxygen saturation. Our study was a single center study with data collected via chart review and phone calls. 309 patients were included in the study. RESULTS: CoSAS proved to be a good score to predict Covid-19 mortality with an Area under the Curve (AUC) of 0.78. It also proved better than qSOFA (AUC of 0.70). More studies are needed to externally validate CoSAS. CONCLUSION: CoSAS is an accurate score to predict Covid-19 mortality in the Pakistani population.

A prediction rule for severe adverse events in all inpatients with community-acquired pneumonia: a multicenter observational study.

BACKGROUND: Prediction of inpatients with community-acquired pneumonia (CAP) at high risk for severe adverse events (SAEs) requiring higher-intensity treatment is critical. However, evidence regarding prediction rules applicable to all patients with CAP including those with healthcare-associated pneumonia (HCAP) is limited. The objective of this study is to develop and validate a new prediction system for SAEs in inpatients with CAP. METHODS: Logistic regression analysis was performed in 1334 inpatients of a prospective multicenter study to develop a multivariate model predicting SAEs (death, requirement of mechanical ventilation, and vasopressor support within 30 days after diagnosis). The developed ALL-COP-SCORE rule based on the multivariate model was validated in 643 inpatients in another prospective multicenter study. RESULTS: The ALL-COP SCORE rule included albumin (< 2 g/dL, 2 points; 2-3 g/dL, 1 point), white blood cell (< 4000 cells/µL, 3 points), chronic lung disease (1 point), confusion (2 points), PaO2/FIO2 ratio (< 200 mmHg, 3 points; 200-300 mmHg, 1 point), potassium (≥ 5.0 mEq/L, 2 points), arterial pH (< 7.35, 2 points), systolic blood pressure (< 90 mmHg, 2 points), PaCO2 (> 45 mmHg, 2 points), HCO3- (< 20 mmol/L, 1 point), respiratory rate (≥ 30 breaths/min, 1 point), pleural effusion (1 point), and extent of chest radiographical infiltration in unilateral lung (> 2/3, 2 points; 1/2-2/3, 1 point). Patients with 4-5, 6-7, and ≥ 8 points had 17%, 35%, and 52% increase in the probability of SAEs, respectively, whereas the probability of SAEs was 3% in patients with ≤ 3 points. The ALL-COP SCORE rule exhibited a higher area under the receiver operating characteristic curve (0.85) compared with the other predictive models, and an ALL-COP SCORE threshold of ≥ 4 points exhibited 92% sensitivity and 60% specificity. CONCLUSIONS: ALL-COP SCORE rule can be useful to predict SAEs and aid in decision-making on treatment intensity for all inpatients with CAP including those with HCAP. Higher-intensity treatment should be considered in patients with CAP and an ALL-COP SCORE threshold of ≥ 4 points. TRIAL REGISTRATION: This study was registered with the University Medical Information Network in Japan, registration numbers UMIN000003306 and UMIN000009837.

Risk assessment of venous thromboembolism and bleeding in COVID-19 patients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is a newly recognized illness that has spread rapidly all over the world. More and more reports highlight the risk of venous thromboembolism (VTE) in COVID-19. Our study aims to identify in-hospital VTE risk and bleeding risk in COVID-19 patients. METHODS: We retrospectively studied 138 consecutively enrolled patients with COVID-19 and identified in-hospital VTE and bleeding risk by Padua Prediction Score and Improve bleed risk assessment model. The clinical data and features were analyzed in VTE patients. RESULTS: Our findings identified that 23 (16.7%) patients with COVID-19 were at high risk for VTE according to Padua prediction score and 9 (6.5%) patients were at high risk of bleeding for VTE prophylaxis according to Improve prediction score. Fifteen critically ill patients faced double high risk from thrombosis (Padua score more than 4 points in all 15 [100%] patients) and hemorrhage (Improve score more than 7 points in 9 [60.0%] patients). Thrombotic events were identified in four patients (2.9%) of all COVID-19 patients. All of them were diagnosed with deep vein thrombosis by ultrasound 3 to 18 days after admission. Three (75.0%) were critically ill patients, which means that the incidence of VTE among critically ill patients was 20%. One major hemorrhage happened in critically ill patients during VTE treatment. CONCLUSION: Critically ill patients with COVID-19 suffered both a high risk of thrombosis and bleeding risks. More effective VTE prevention strategies based on an individual assessment of bleeding risks were necessary for critically ill patients with COVID-19.

Generation and validation of in-hospital mortality prediction score in COVID-19 patients: Alba-score.

BACKGROUND: COVID-19 has a wide range of symptoms reported, which may vary from very mild cases (even asymptomatic) to deadly infections. Identifying high mortality risk individuals infected with the SARS-CoV-2 virus through a prediction instrument that uses simple clinical and analytical parameters at admission can help clinicians to focus on treatment efforts in this group of patients. METHODS: Data was obtained retrospectively from the electronic medical record of all COVID-19 patients hospitalized in the Albacete University Hospital Complex until July 2020. Patients were split into two: a generating and a validating cohort. Clinical, demographical and laboratory variables were included. A multivariate logistic regression model was used to select variables associated with in-hospital mortality in the generating cohort. A numerical and subsequently a categorical score according to mortality were constructed (A: mortality from 0% to 5%; B: from 5% to 15%; C: from 15% to 30%; D: from 30% to 50%; E: greater than 50%). These scores were validated with the validation cohort. RESULTS: Variables independently related to mortality during hospitalization were age, diabetes mellitus, confusion, SaFiO2, heart rate and lactate dehydrogenase (LDH) at admission. The numerical score defined ranges from 0 to 13 points. Scores included are: age ≥71 years (3 points), diabetes mellitus (1 point), confusion (2 points), onco-hematologic disease (1 point), SaFiO2 ≤ 419 (3 points), heart rate ≥ 100 bpm (1 point) and LDH ≥ 390 IU/L (2 points). The area under the curve (AUC) for the numerical and categorical scores from the generating cohort were 0.8625 and 0.848, respectively. In the validating cohort, AUCs were 0.8505 for the numerical score and 0.8313 for the categorical score. CONCLUSIONS: Data analysis found a correlation between clinical admission parameters and in-hospital mortality for COVID-19 patients. This correlation is used to develop a model to assist physicians in the emergency department in the COVID-19 treatment decision-making process.

The PANDEMYC Score. An Easily Applicable and Interpretable Model for Predicting Mortality Associated With COVID-19.

This study aimed to build an easily applicable prognostic model based on routine clinical, radiological, and laboratory data available at admission, to predict mortality in coronavirus 19 disease (COVID-19) hospitalized patients. METHODS: We retrospectively collected clinical information from 1968 patients admitted to a hospital. We built a predictive score based on a logistic regression model in which explicative variables were discretized using classification trees that facilitated the identification of the optimal sections in order to predict inpatient mortality in patients admitted with COVID-19. These sections were translated into a score indicating the probability of a patient's death, thus making the results easy to interpret. RESULTS: Median age was 67 years, 1104 patients (56.4%) were male, and 325 (16.5%) died during hospitalization. Our final model identified nine key features: age, oxygen saturation, smoking, serum creatinine, lymphocytes, hemoglobin, platelets, C-reactive protein, and sodium at admission. The discrimination of the model was excellent in the training, validation, and test samples (AUC: 0.865, 0.808, and 0.883, respectively). We constructed a prognostic scale to determine the probability of death associated with each score. CONCLUSIONS: We designed an easily applicable predictive model for early identification of patients at high risk of death due to COVID-19 during hospitalization.

A case of non-severe COVID-19 complicated by pulmonary embolism.

Novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 is rapidly spreading worldwide. A typical clinical manifestation of COVID-19 is pneumonia, which can progress to acute respiratory distress syndrome and respiratory failure. Recent studies have reported that COVID-19 is often accompanied by coagulopathy, and a significant number of patients with severe or critical COVID-19 develop concomitant thrombosis, including pulmonary embolism (PE). However, there are limited reports of the incidence of PE in non-severe COVID-19 patients. Here, we report a case of non-severe COVID-19 complicated by PE, which indicates that the possibility of PE should consistently be considered, even in non-severe cases of COVID-19 without any risk of thrombosis.