Clinical Prediction Score Research Articles

Development of Ebola virus disease prediction scores: Screening tools for Ebola suspects at the triage-point during an outbreak.

The control of Ebola virus disease (EVD) outbreaks relies on rapid diagnosis and prompt action, a daunting task in limited-resource contexts. This study develops prediction scores that can help healthcare workers improve their decision-making at the triage-point of EVD suspect-cases during EVD outbreaks. We computed accuracy measurements of EVD predictors to assess their diagnosing ability compared with the reference standard GeneXpert® results, during the eastern DRC EVD outbreak. We developed predictive scores using the Spiegelhalter-Knill-Jones approach and constructed a clinical prediction score (CPS) and an extended clinical prediction score (ECPS). We plotted the receiver operating characteristic curves (ROCs), estimated the area under the ROC (AUROC) to assess the performance of scores, and computed net benefits (NB) to assess the clinical utility (decision-making ability) of the scores at a given cut-off. We performed decision curve analysis (DCA) to compare, at a range of threshold probabilities, prediction scores' decision-making ability and to quantify the number of unnecessary isolation. The analysis was done on data from 10432 subjects, including 651 EVD cases. The EVD prevalence was 6.2% in the whole dataset, 14.8% in the subgroup of suspects who fitted the WHO Ebola case definition, and 3.2% for the set of suspects who did not fit this case definition. The WHO clinical definition yielded 61.6% sensitivity and 76.4% specificity. Fatigue, difficulty in swallowing, red eyes, gingival bleeding, hematemesis, confusion, hemoptysis, and a history of contact with an EVD case were predictors of EVD. The AUROC for ECPS was 0.88 (95%CI: 0.86-0.89), significantly greater than this for CPS, 0.71 (95%CI: 0.69-0.73) (p < 0.0001). At -1 point of score, the CPS yielded a sensitivity of 85.4% and specificity of 42.3%, and the ECPS yielded sensitivity of 78.8% and specificity of 81.4%. The diagnostic performance of the scores varied in the three disease contexts (the whole, fitting or not fitting the WHO case definition data sets). At 10% of threshold probability, e.g. in disease-adverse context, ECPS gave an NB of 0.033 and a net reduction of unnecessary isolation of 67.1%. Using ECPS as a joint approach to isolate EVD suspects reduces the number of unnecessary isolations by 65.7%. The scores developed in our study showed a good performance as EVD case predictors since their use improved the net benefit, i.e., their clinical utility. These rapid and low-cost tools can help in decision-making to isolate EVD-suspicious cases at the triage point during an outbreak. However, these tools still require external validation and cost-effectiveness evaluation before being used on a large scale.

465 LONG-TERM MAYOR ADVERSE CARDIOVASCULAR EVENTS AFTER COVID-19 INFECTION: A CLINICAL SCORE FROM A COHORT OF 2575 PATIENTS ENROLLED IN THE MULTICENTER INTERNATIONAL HOPE 2 REGISTRY

Abstract Background Long-term consequences of COVID-19 infection are still partly known. According to some studies several patients may experience long term symptoms; however, predictors of long-term mayor adverse cardiovascular events among (MACE) patients with previous COVID-19 infection are unknown. Aim of the study To derive a simple clinical score for risk prediction of long-term MACE among patients with previous covid-19 infection. Methods 2575 consecutive patients were enrolled in a multicenter, international registry (HOPE-2) from February 2020 to April 2021, and followed-up at long-term. A risk score was developed using a stepwise multivariable regression analysis. Results Out of 2575 patients enrolled in the HOPE-2 registry, 1481 (58%) were male, with mean age of 60±16 years. At long-term follow-up overall rate of MACE was 7.9% (202 of 2545 pts, 3.8% death, 1.3% coronary artery disease, 1.2% heart failure admission, 0.9% cardiac arrhythmias, 0.8% venous thrombosis, 0.7% cerebrovascular accident). After multivariable regression analysis, independent predictors of MACE were used to derive a simple prognostic score. The HOPE-2 prognostic score may be calculated by giving: ½ point for every 10 years of age, 2 points for previous cardiovascular disease, 1 point for increased troponin serum levels during hospitalization, 2.5 points for heart failure and 3 points for sepsis during hospitalization, -1.5 points for vaccination at follow-up. Score accuracy at receiver operating characteristic curve analysis was 0.81. Stratification into 3 risk groups (0-2, 3-5, and &amp;gt;5 points) classified into low, intermediate and high risk. The observed MACE rates were 0.5% for low-risk patients, 4% for intermediate-risk patients, and 19.5% for high-risk patients (log-Rank p&amp;lt;0.001). Conclusions The HOPE-2 prognostic score may be useful for long-term risk stratification in patients with previous COVID-19 infection. High-risk patients may require a strict cardiological follow-up.

294. Risk factors of COVID-19 associated pulmonary aspergillosis in a high endemic setting and development of a bedside clinical risk prediction score.

Abstract Background India has a high burden of invasive fungal infections. Although invasive aspergillosis was also reported during the COVID-19 pandemic, the real world data on the risk factors and outcome of CAPA are limited. Our aim is to determine risk factors and clinical outcomes of CAPA and develop a prediction model for patient stratification. Methods A retrospective, case-control study was conducted at a 1300-bedded South Indian tertiary care academic centre from June 1st, 2020 to May 31st, 2021. CAPA cases were defined by 2020 ECMM/ISHAM consensus criteria as possible, probable, and proven infection. Age and admission period matched control group with COVID-19 but without aspergillosis was selected in a 1:1 ratio. A risk scoring stratification for CAPA was developed based on the significant CAPA risk factors using logistic regression model. Figure 1:Receiving operating characteristic curve of CAPA incidence score for predicting CAPA in the study cohort Results 95 CAPA cases, of which 75(79%) were probable and 20(21%) possible, were diagnosed during the study period. 84(88.4%) patients had moderate to severe COVID-19, and 75(78.9%) were treated with steroids, most commonly dexamethasone (Table 1). The time from COVID-19 diagnosis to CAPA was 13 days (IQR 12). 40(42.1%) of patients were on mechanical ventilation at CAPA diagnosis. Outcome measures (MV, NIV and hospital/ICU stay) were significantly higher in CAPA patients compared to controls(Table 2). Neutropenia, use of steroids, broad spectrum antibiotic use, fluconazole prophylaxis and absence of co-infecting pathogen were found to be significant factors associated with CAPA(p&amp;lt; 0.05) (Table 3). An optimal risk score of ≥10.00 predicted CAPA with a sensitivity of 84.2% and a specificity of 59% with an area under the curve of 0.77 (PPV=67.23%, NPV=78.87%)(Fig 1). 75(78.9%) patients had positive serum aspergillus galactomannan with an average value of 1.89±1.65. 28-day (41.1% vs 33.7%, p=0.13) and 6-week all cause-mortality (48.4% vs 37.9%, p=0.07) were higher, but not statistically significant, for CAPA. Table 1:Baseline characteristics and risk factorsTable 2:Primary and secondary outcomesTable 3:CAPA incidence scoring table Significant univariate variables were included in the multivariate logistic regression model and predicted probabilities based on the beta co-efficients of the significant variables in the model were transformed to generate CAPA incidence score for patients. Conclusion Risk factors of CAPA in Indian were similar to those reported previously in other countries. CAPA can be seen in severe COVID-19 patients who are not mechanically ventilated. A CAPA risk scoring system, that needs external validation, is a simple and feasible tool that could be useful in stratification of patients suspected of CAPA. Disclosures All Authors: No reported disclosures.

1886. External Validation of the 4C Mortality Score and the qSOFA for Different Variants of Concerns of SARS-CoV-2 Using Data of the NAPKON Cross-Sectoral Cohort Platform (SUEP)

Abstract Background Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1:Study flow chart with inclusion criteria and methodological workflow. Results Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970); delta: AUC 0.769 (95%CI 0.657-0.882); omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1:Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV-2. Conclusion Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size. Disclosures Daniel Pape, Dr., Advanz Pharma Germany: Support for attending meetings and/or travel for ECCMID 2021 Martin Hower, n/a, MSD: Advisor/Consultant|Trogarzo: Advisor/Consultant|ViiV Healthcare: Advisor/Consultant Björn-Erik O. Jensen, Dr. med., GILEAD: Advisor/Consultant|GILEAD: Lectures, Travel|GSK: Lectures, Travel Jörg J. Vehreschild, Univ.-Prof. Dr. med., Ärztekammer Nordrhein: Honoraria|Academy for Infectious Medicine, University Manchester: Honoraria|Astellas Pharma: Grant/Research Support|Astellas Pharma: Honoraria|Back Bay Strategies: Honoraria|Basilea: Grant/Research Support|Basilea: Honoraria|Deutsches Zetrum für Luft- und Raumfahrt (DLR): Grant/Research Support|German Centre for Infection Research (DZIF): Grant/Research Support|German Centre for Infection Research (DZIF): Honoraria|German Federal Ministry of Education and Research (BMBF): Grant/Research Support|German Society for Infectious Diseases (DGI): Honoraria|German Society for Internal Medicine (DGIM): Honoraria|GILEAD: Advisor/Consultant|GILEAD: Grant/Research Support|GILEAD: Honoraria|Janssen: Honoraria|Merck / MSD: Grant/Research Support|Merck / MSD: Honoraria|Molecular Health: Honoraria|Netzwerk Universitätsmedizin: Honoraria|NordForsk: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Rigshospitalet Copenhagen: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Honoraria|University Hospital Aachen: Honoraria|University Hospital Freiburg/ Congress and Communication: Honoraria|University of Bristol: Grant/Research Support.

EPH2 Development and Validation of Clinical Prediction Score of Mortality in Tuberculosis Patients

EPH2 Development and Validation of Clinical Prediction Score of Mortality in Tuberculosis Patients

Association of Continuously Measured Vital Signs With Respiratory Insufficiency in Hospitalized COVID-19 Patients: Retrospective Cohort Study.

Continuous monitoring of vital signs has the potential to assist in the recognition of deterioration of patients admitted to the general ward. However, methods to efficiently process and use continuously measured vital sign data remain unclear. The aim of this study was to explore methods to summarize continuously measured vital sign data and evaluate their association with respiratory insufficiency in COVID-19 patients at the general ward. In this retrospective cohort study, we included patients admitted to a designated COVID-19 cohort ward equipped with continuous vital sign monitoring. We collected continuously measured data of respiratory rate, heart rate, and oxygen saturation. For each patient, 7 metrics to summarize vital sign data were calculated: mean, slope, variance, occurrence of a threshold breach, number of episodes, total duration, and area above/under a threshold. These summary measures were calculated over timeframes of either 4 or 8 hours, with a pause between the last data point and the endpoint (the "lead") of 4, 2, 1, or 0 hours, and with 3 predefined thresholds per vital sign. The association between each of the summary measures and the occurrence of respiratory insufficiency was calculated using logistic regression analysis. Of the 429 patients that were monitored, 334 were included for analysis. Of these, 66 (19.8%) patients developed respiratory insufficiency. Summarized continuously measured vital sign data in timeframes close to the endpoint showed stronger associations than data measured further in the past (ie, lead 0 vs 1, 2, or 4 hours), and summarized estimates over 4 hours of data had stronger associations than estimates taken over 8 hours of data. The mean was consistently strongly associated with respiratory insufficiency for the three vital signs: in a 4-hour timeframe without a lead, the standardized odds ratio for heart rate, respiratory rate, and oxygen saturation was 2.59 (99% CI 1.74-4.04), 5.05 (99% CI 2.87-10.03), and 3.16 (99% CI 1.78-6.26), respectively. The strength of associations of summary measures varied per vital sign, timeframe, and lead. The mean of a vital sign showed a relatively strong association with respiratory insufficiency for the majority of vital signs and timeframes. The type of vital sign, length of the timeframe, and length of the lead influenced the strength of associations. Highly associated summary measures and their combinations could be used in a clinical prediction score or algorithm for an automatic alarm system.

Development of a Clinical Risk Score Prediction Tool for 5-, 9-, and 13-Year Risk of Dementia

Although researchers have devoted substantial efforts, money, and time to studying the causes of dementia and the means to prevent it, no effective treatment exists yet. Identifying preclinical risk factors of dementia could help prevent or delay its progression. To develop a point risk score prediction model of dementia. This study used a large UK population-based prospective cohort study conducted between March 13, 2006, and October 1, 2010. Data analysis was performed from June 7 to September 15, 2021. Individual analyses of time end points were concluded at the first dementia diagnosis during the follow-up period. The data were split into training and testing data sets to separately establish and validate a prediction model. Outcomes of interest included 5-, 9-, and 13-year dementia risk. Least absolute shrinkage and selection operator and multivariate Cox proportional hazards regression models were used to identify available and practical dementia predictors. A point risk score model was developed for the individual prediction of 5-, 9-, and 13-year dementia risk. A total of 502 505 participants were selected; the population after exclusions for missing data and dementia diagnosis at baseline was 444 695 (205 187 men; mean [SD] age, 56.74 [8.18] years; 239 508 women; mean [SD] age, 56.20 [8.01] years). Dementia occurrence during the 13 years of follow-up was 0.7% for men and 0.5% for women. The C statistic of the final multivariate Cox proportional hazards regression model was 0.86 for men and 0.85 for women in the training data set, and 0.85 for men and 0.87 for women in the testing data set. Men and women shared some modifiable risk and protective factors, but they also presented independent risk factors that accounted for 31.7% of men developing dementia and 53.35% of women developing dementia according to the weighted population-attributable fraction. The total point score of the risk score model ranged from -18 to 30 in men and -17 to 30 in women. The risk score model yielded nearly 100% prediction accuracy of 13-year dementia risk both in men and women. In this diagnostic study, a practical risk score tool was developed for individual prediction of dementia risk, which may help individuals identify their potential risk profile and provide guidance on precise and timely actions to promote dementia delay or prevention.

Validation of Existing Clinical Prediction Tools for Primary Aldosteronism Subtyping.

The new clinical prediction score (SCORE) has been recently proposed for primary aldosteronism (PA) subtyping prior to adrenal vein sampling (AVS). This study aimed to compare that SCORE with previously published scores and their validation using a cohort of patients at our center who had had positive SIT confirming PA and had been diagnosed with either bilateral PA according to AVS or unilateral PA if biochemically cured after an adrenalectomy. Final diagnoses were used to evaluate the diagnostic performance of the proposed clinical prediction tools. Only Kamemura's model (with a maximum score of 4 points) and Kobayashi's score (with a maximum score of 12 points) reached 100% reliability for prediction of bilateral PA; however, with sensitivity of only 3%. On the other hand, the values of SCORE = 3 (with sensitivity of 48%), the SPACE score ≥18 (with sensitivity of 35%), the Kobayashi's score ≤2 (with sensitivity of 28%), and the Kocjan's score = 3 (with sensitivity of 28%) were able to predict unilateral PA with 100% probability. Furthermore, Umakoshi's and Young's models both reached 100% reliability for a unilateral PA with score = 4 and both predictive factors together respectively; however, the sensitivity was lower compared with previous models; 4% and 14%, respectively. None of the clinical prediction tools applied to our cohort predicted unilateral and bilateral subtypes together with the expected high diagnostic performance, and therefore can only be used for precisely defined cases.

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

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. 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. 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). The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

Prediction of Death after Terminal Extubation, the Machine Learning Way

Introduction: Terminal Extubation (TE), as hospital End of Life (EOL) policies is the withdrawal of ventilatory support from terminally ill patients. Although TE and its natural progression to death provides closure to the suffering for patient and the family, we lack the tools to serve that purpose. This inability also negatively affects Donation after Cardiac Death organ donation efforts and making death vigil more grueling than comforting. We developed an advanced machine learning model, long-short term memory (LSTM) to accurately predict death within 2 hours post-TE and compare to the standard regression model on priori list of clinical variables. Methods: Clinical, laboratory, demographic data from 1,965 patients were extracted 24 hours before TE. Validation set (1,645 patients at Yale New-Haven Hospital) and external validation set(320 patients at Bridgeport-hospital). Outcome of interest was death within 2 hours of TE. LSTM model was trained on different versions of time-updating data, including carryforward and zero imputation after data transformation. LSTM model was compared with clinical prediction score. Results: LSTM model outperformed clinical prediction score (AUC 0.66 vs 0.51, P = 0.001) in predicting post-extubation death within 2 hours on external validation. At matched sensitivity threshold of 94%(with clinical prediction score of ≥2), LSTM model outperformed clinical prediction score(specificity 28.8% vs 11.1%). Conclusion: We were able to accurately predict progression to natural death within 2 hours after TE and our LSTM model was superior to clinical prediction score. This information will critically help intensivists facilitate EOL discussions, improve organ donation rates and help in operationalizing allocation of ICU resources efficiently.

Development of a Clinical Prediction Score Including Monocyte-to-Lymphocyte Ratio to Inform Tuberculosis Treatment Among Children With HIV: A Multicountry Study.

Clinical pediatric tuberculosis (TB) diagnosis may lead to overdiagnosis particularly among children with human immunodeficiency virus (CHIV). We assessed the performance of monocyte-lymphocyte ratio (MLR) as a diagnostic biomarker and constructed a clinical prediction score to improve specificity of TB diagnosis in CHIV with limited access to microbiologic testing. We pooled data from cohorts of children aged ≤13 years from Vietnam, Cameroon, and South Africa to validate the use of MLR ≥0.378, previously found as a TB diagnostic marker among CHIV. Using multivariable logistic regression, we created an internally validated prediction score for diagnosis of TB disease in CHIV. The combined cohort had 601 children (median age, 1.9 [interquartile range, 0.9-5.3] years); 300 (50%) children were male, and 283 (47%) had HIV. Elevated MLR ≥0.378 had sensitivity of 36% (95% confidence interval [CI], 23%-51%) and specificity of 79% (95% CI, 71%-86%) among CHIV in the validation cohort. A model using MLR ≥0.28, age ≥4 years, tuberculin skin testing ≥5 mm, TB contact history, fever >2 weeks, and chest radiograph suggestive of TB predicted active TB disease in CHIV with an area under the receiver operating characteristic curve of 0.85. A prediction score of ≥5 points had a sensitivity of 94% and specificity of 48% to identify confirmed TB, and a sensitivity of 82% and specificity of 48% to identify confirmed and unconfirmed TB groups combined. Our score has comparable sensitivity and specificity to algorithms including microbiological testing and should enable clinicians to rapidly initiate TB treatment among CHIV when microbiological testing is unavailable.

Validation of Clinical Prediction Scores for Pediatric Appendicitis: Update with New Cutoff Values for the AIR Score.

Gudjonsdottir et al validated four clinical prediction scores for appendicitis in children. The AIR score and pARC had the best performance. A recent validation of the AIR score (AIR2) recommended an adjustment of the low risk interval. With the new risk interval the PAS and AIR2 score assign about half of the no appendicitis patients to the low risk group with very high sensitivity for advanced appendicitis (0.97-0.99). Expectant management with planned reexamination, can thus safely be considered for this group. The AIR2 score and pARC both assign a smaller proportion of patients to the high risk groups (14 and 22%, respectively) but with high specificity (0.93 and 0.98). At such a high specificity a diagnostic laparoscopy should be considered before imaging, as a negative imaging study can probably not rule out appendicitis.

Long-term mayor adverse cardiovascular events after COVID-19 infection: a clinical score from a cohort of 2575 patients enrolled in the multicenter international HOPE 2 registry

Abstract Background Long-term consequences of COVID-19 infection are still partly known. According to some studies several patients may experience long term symptoms; however, predictors of long-term mayor adverse cardiovascular events among (MACE) patients with previous COVID-19 infection are . Aim of the study To derive a simple clinical score for risk prediction of long-term MACE among patients with previous covid-19 infection. Methods 2575 consecutive patients were enrolled in a multicenter, international registry (HOPE-2) from February 2020 to April 2021, and followed-up at long-term. A risk score was developed using a stepwise multivariable regression analysis. Results Out of 2575 patients enrolled in the HOPE-2 registry, 1481 (58%) were male, with mean age of 60±16 years. At long-term follow-up overall rate of MACE was 7.9% (202 of 2545 pts, 3.3% death, 2.4% inflammatory myocardial disease, 1.3% arterial thrombosis, 0.7% venous thrombosis). After multivariable regression analysis, independent predictors of MACE were used to derive a simple prognostic score: The HOPE-2 prognostic score may be calculated by giving: ½ point for every 10 years of age, 2 points for previous cardiovascular disease, 1 point for increased troponin serum levels during hospitalization, 2.5 points for heart failure and 3 points for sepsis during hospitalization, −1.5 points for vaccination at follow-up. Score accuracy at receiver operating characteristic curve analysis was 0.81. Stratification into 3 risk groups (0–2, 3–5, and &amp;gt;5 points) classified into low, intermediate and high risk. The observed MACE rates were 0.5% for low-risk patients, 4% for intermediate-risk patients, and 19.5% for high-risk patients (log-Rank p&amp;lt;0.001, Figure 1). Conclusions The HOPE-2 prognostic score may be useful for long-term risk stratification in patients with previous COVID-19 infection. High-risk patients may require a strict cardiological follow-up. Funding Acknowledgement Type of funding sources: None.

A novel clinical and cardiac magnetic resonance risk score for early risk prediction after ST-segment elevation myocardial infarction

Abstract Background Cardiac magnetic resonance (CMR) performed early after ST-segment elevation myocardial infarction (STEMI) can improve major adverse cardiac event (MACE) risk prediction. However, predictive models including clinical and CMR variables are scarce and not routinely implemented in clinical practice. Purpose We aimed to create a simple clinical-CMR risk score for early MACE risk stratification in STEMI patients. Methods We performed a multicenter prospective registry in three Spanish university hospitals of reperfused STEMI patients (n=1118) in whom early (1-week) CMR-derived left ventricular ejection fraction (LVEF), infarct size and microvascular obstruction (MVO) were quantified. MACE was defined as a combined clinical endpoint of cardiovascular (CV) death, non-fatal myocardial infarction (NF-MI) or re-admission for acute decompensated heart failure (HF), whichever occurred first. Univariate and multivariate analyses were performed and a risk score was computed using the variables which independently predicted the risk of MACE. Results During a median follow-up of 5.52 [2.63–7.44] years, 216 first MACE (58 CV deaths, 71 NF-MI and 87 HF) were registered. Mean age was 59.3±12.3 years and most patients (82.8%) were male. Based on the four variables independently associated with MACE, we computed an 8-point risk score: time to reperfusion &amp;gt;4.15h (1 point), GRACE risk score &amp;gt;155 (3 points), CMR-LVEF &amp;lt;40% (3 points), and MVO &amp;gt;1.5 segments (1 point). This score permitted MACE risk stratification: MACE per 100 person-years was 1.96 in the low-risk category (0–2 points), 5.44 in the intermediate-risk category (3–5 points), and 19.7 in the high-risk category (6–8 points): p&amp;lt;0.001 in multivariable Cox survival analysis. Conclusions A novel risk score including clinical (time to reperfusion &amp;gt;4.15h and GRACE risk score &amp;gt;155) and CMR (LVEF &amp;lt;40% and MVO &amp;gt;1.5 segments) variables allows for simple and straightforward MACE risk stratification early after STEMI. External validation should confirm the applicability of the risk score. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Instituto de Salud Carlos III and Fondo Europeo de Desarrollo Regional (FEDER) and Sociedad Española de Cardiología.

Monocyte-to-lymphocyte ratio affects prognosis in LAA-type stroke patients

Nowadays, the prognostic prediction of acute ischemic stroke (AIS) patients is still challenging because of the limited predictive properties of existing models. Blood-based biomarkers may provide additional information to the established prognostic factors. Markers of atherosclerosis have been identified as one of the most promising biomarkers for predicting prognosis, and inflammation, in turn, affects atherosclerosis. According to previous studies, the ratio of monocytes to lymphocytes (MLR) has been reported as a novel indicator of inflammation. Thus, our study was the first to conduct more in-depth research on the relationship between MLR and the prognosis of large artery atherosclerosis (LAA)-type AIS patients.A total of 296 patients with LAA-type stroke were recruited. Of these, 202 patients were assigned to the development cohort, and 94 patients were assigned to the validation cohort. In the development cohort, 202 patients were divided into groups A, B, C, and D according to the quartile method of MLR levels. The one-year prognosis of patients was tracked, and the modified Rankin scale (MRS, with a score ranging from 0 to 6) was mainly selected as the measurement result of the function. The relationship between MLR and prognosis was analyzed by building logistics regression models. The models showed that MLR made significant predictions in poor outcomes of LAA-type stroke patients (odds ratio: 4.037; p = 0.048). At the same time, receiver operating characteristics (ROC) curves were used to compare the predictive values between MLR and clinical prediction score (Barthel Index).This study demonstrated that patients with LAA-type stroke and high MLR had a poor prognosis. MLR might be a reliable, inexpensive, and novel predictor of LAA-type stroke prognosis.

S450 Assessment of the Accuracy of a Clinical Risk Prediction (Kunzmann) Score 5 Years Prior to Barrett’s Esophagus and Esophageal Adenocarcinoma Diagnosis: Results From a Large Population-Based Database

S450 Assessment of the Accuracy of a Clinical Risk Prediction (Kunzmann) Score 5 Years Prior to Barrett’s Esophagus and Esophageal Adenocarcinoma Diagnosis: Results From a Large Population-Based Database

Development and validation of COVID-19 associated candidemia score (CAC-Score) in ICU patients.

BackgroundThe development of candidemia is a highly fatal condition in severe COVID‐19 infection.ObjectivesThis study aimed to develop a candidemia prediction score in COVID‐19 patient based on the patient's clinical characteristics, and healthcare‐related factors during intensive care units (ICU) follow‐up.Patients/MethodsSevere COVID‐19 patients hospitalised in ICU in Ankara City Hospital during the one‐year period (August 15, 2020, and August 15, 2021) were included. After univariate analysis, multivariate analysis was applied using variable selection approach to investigate the effects of variables together and to create a score model for candidemia. Statistically significant factors were included in the development process of candida prediction score.ResultsOf 1305 COVID‐19 ICU patients, 139 had a candidemia episode. According to the final model, four variables, presence of central venous catheter (CVC) (OR 19.07, CI 8.12–44.8, p < .0001), multifocal colonisation (OR 2.28, CI 1.39–3.72, p 0.001), length of ICU stays ≥14 days (OR 3.62, CI 2.42–5.44, p < .0001) and corticosteroids (OR 0.51, CI 0.34–0.76, p 0.0011) were the only statistically significant independent risk factors for candidemia. Score model was demonstrated by a nomogram, and the risk for candidemia was calculated to be high in patients who scored ≥56 points by using the criteria [CVC = 51, multifocal colonisation = 14, prolonged hospitalisation = 23, no steroid use = 12 points]. The AUC of the score is 0.84 (CI 0.81–0.87).ConclusionWe developed and validated an easy‐to‐use clinical prediction score for candidemia in severe COVID‐19 infection. In COVID‐19 ICU patients, the risk of candidemia is high if one of the other risk factors is present together with CVC.

Predicting success of intrauterine insemination using a clinically based scoring system

PurposeTo develop a predictive score for the success of intrauterine insemination (IUI) based on clinical parameters.MethodsWe performed a retrospective cohort study evaluating the homologous IUI cycles performed at a single university-based reproductive medical center between 2009 and 2017. The primary outcome measure was pregnancy, defined as positive serum human chorionic gonadotropin (hCG) 12–14 days after IUI. Predictive factors for pregnancy after IUI were identified, and a predictive score was developed using a multivariable continuation ratio model.ResultsOverall, 1437 IUI cycles in 758 couples were evaluated. We found a per cycle pregnancy rate of 10.9% and a cumulative pregnancy rate of 19.4%. In a multivariable analysis, the probability of pregnancy was negatively associated with female age ≥ 35 years (OR 0.63, 95% CI 0.41–0.97, p = 0.034), endometriosis, unilateral tubal factor, or anatomical alteration (OR 0.54, 95% CI 0.33–0.89, p = 0.016), anti-Mullerian hormone (AMH) < 1 ng/ml (OR 0.50, 95% CI 0.29–0.87, p = 0.014), and total progressive motile sperm count (TPMSC) < 5 mil (OR 0.47, 95% CI 0.19–0.72, p = 0.004). We developed a predictive clinical score ranging from 0 to 5. Following 3 cycles, couples in our cohort with a score of 5 had a cumulative probability of achieving pregnancy of nearly 45%. In contrast, couples with a score of 0 had a cumulative probability of only 5%.ConclusionIUI success rates vary widely depending on couples’ characteristics. A simple to use score could be used to estimate a couple’s chance of achieving pregnancy via IUI, facilitating individualized counseling and decision-making.

Prediction of clinical orthopaedic scores based on instrumental gait analysis

Prediction of clinical orthopaedic scores based on instrumental gait analysis

EP08.04-003 A Simple Clinical Prediction Score to Determine Response to First Line Chemotherapy Treatment in Advanced Non-Small Cell Lung Cancer Patients

The outcomes of advanced non-small cell lung cancer (NSCLC) patients have been significantly improved with novel therapies such as tyrosine kinase inhibitors and immune checkpoint inhibitors. However, in many limited-resource countries, platinum-doublets chemotherapy is mainly used as the first line regimen. We investigate several clinical parameters to predict the response after chemotherapy which may be useful for physician in patient selection.