Development of a clinical risk score for the prediction of Pneumocystis jirovecii pneumonia in hospitalised patients.

BACKGROUND: The performance and availability of invasive and non-invasive investigations for the diagnosis of Pneumocystis jirovecii pneumonia (PCP) vary across clinical settings. Estimating the pre-test probability of PCP is essential to the optimal selection and interpretation of diagnostic tests, such as the 1,3-ß-D-glucan assay (BDG), for the prioritization of bronchoscopy, and to guide empiric treatment decisions. We aimed to develop a multivariable risk score to estimate the pre-test probability of PCP. METHODS: The score was developed from a cohort of 626 individuals who underwent bronchoscopy for the purposes of identifying PCP in a Canadian tertiary-care centre, between 2015 and 2018. We conducted a nested case-control study of 57 cases and 228 unmatched controls. Demographic, clinical, laboratory, and radiological data were included in a multivariable logistic regression model to estimate adjusted odds ratios for PCP diagnosis. A clinical risk score was derived from the multivariable model and discrimination was assessed by estimating the score's receiver operating characteristic curve. RESULTS: Participants had a median age of 60 years (interquartile range [IQR] 49-68) and 115 (40%) were female; 40 (14%) had HIV and 49 (17%) had a solid organ transplant (SOT). The risk score included prior SOT or HIV with CD4 ≤ 200/µL (+ 2), serum lactate dehydrogenase ≥ 265.5 IU/mL (+ 2), radiological pattern typical of PCP on chest x-ray (+ 2) or CT scan (+ 2.5), and PCP prophylaxis with trimethoprim-sulfamethoxazole (-3) or other antimicrobials (-2). The median score was 4 points (IQR, 2-4.5) corresponding to a 28% probability of PCP. The risk prediction model had good discrimination with a c-statistic of 0.79 (0.71-0.84). Given the operating characteristics of the BDG assay, scores ≤ 3 in patients without HIV, and ≤ 5.5 in those with HIV, paired with a negative BDG, would be expected to rule out PCP with 95% certainty. CONCLUSION: We propose the PCP Score to estimate pre-test probability of PCP. Once validated, it should help clinicians determine which patients to refer for invasive investigations, when to rely on serological testing, and in whom to consider pre-emptive treatment.

Development and validation of a clinical risk score nomogram for predicting voriconazole trough concentration above 5 mg/L: a retrospective cohort study.

The therapeutic range of voriconazole (VRC) is narrow, this study aimed to explore factors influencing VRC plasma concentrations > 5 mg/L and to construct a clinical risk score nomogram prediction model. Clinical data from 221 patients with VRC prophylaxis and treatment were retrospectively analyzed. The patients were randomly divided into a training cohort and a validation cohort at a 7:3 ratio. Univariate and binary logistic regression analysis was used to select independent risk factors for VRC plasma concentration above the high limit (5 mg/L). Four indicators including age, weight, CYP2C19 genotype, and albumin were selected to construct the nomogram prediction model. The area under the curve values of the training cohort and the validation cohort were 0.841 and 0.802, respectively. The decision curve analysis suggests that the nomogram model had good clinical applicability. In conclusion, the nomogram provides a reference for early screening and intervention in a high-risk population.

Vital Signs as Predictors of Oxygen Requirements in COVID-19 Outpatients: The Development of a Streamlined Risk Score.

BACKGROUND: With COVID-19 becoming a common disease, primary care facilities such as clinics are required to efficiently triage patients at high risk of severe illness within the constraints of limited medical resources. However, existing COVID-19 severity risk scores require detailed medical history assessments, such as evaluating the severity of pneumonia via chest CT and accounting for past and comorbid conditions. Therefore, they may not be suitable for practical use in clinical settings with limited medical resources, including personnel and equipment. PURPOSE:  The goal is to identify key variables that predict the need for oxygen therapy in COVID-19 patients and develop a simplified clinical risk score based solely on vital signs to predict oxygen requirements. PATIENTS AND METHODS: A retrospective observational study of 584 outpatients with COVID-19 confirmed by polymerase chain reaction test visited Sasebo Chuo Hospital between April 28, 2022, and August 18, 2022. Analyses were conducted after adjustment for background factors of age and sex with propensity score matching. We used the Fisher test for nominal variables and the Kruskal-Wallis test for continuous variables. RESULTS: After adjusting for age and sex, several factors significantly correlated with the need for oxygen within seven days including body temperature (p < 0.001), respiratory rate (p = 0.007), SpO2 (p < 0.001), and the detection of pneumonia on CT scans (p = 0.032). The area under the receiver-operating characteristic curve for the risk score based on these vital signs and CT was 0.947 (95% confidence interval: 0.911-0.982). The risk score based solely on vital signs was 0.937 (0.900-0.974), demonstrating the ability to predict oxygen administration with no significant differences. CONCLUSIONS: Body temperature, advanced age, increased respiratory rate, decreased SpO2, and the presence of pneumonia on CT scans were significant predictors of oxygen need within seven days among the study participants. The risk score, based solely on vital signs, effectively and simply assesses the likelihood of requiring oxygen therapy within seven days with high accuracy. The risk score, which utilizes only age and vital signs and does not require a detailed patient history or CT scans, could streamline hospital referral processes for admissions.

Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians.

We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations.

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRSAI) and Europeans (PRSEU) using 13,974 AI individuals. Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results: Both genetic models (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRSAI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10-21) higher predictability to identify subgroups with higher genetic risk than the PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU. Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.

Predictive Factors for the Survival Outcomes of Preoperative Chemotherapy in Patients with Resectable and Borderline Resectable Colorectal Cancer with Liver Metastasis.

BACKGROUND: Preoperative chemotherapy increases resectability in borderline resectable colorectal liver metastasis (CRLM) patients who undergo curative liver surgery. Most clinical risk scores and other predictive factors for survival have been extensively studied in patients who undergo upfront liver surgery. However, predictive factors of CRLM patients who received preoperative chemotherapy remains controversial. METHODS: CRLM patients who received preoperative systemic therapy followed by curative liver surgery at our institution between 1/2012 and 12/2018 were included. This study aimed to investigate factors that predicted the outcomes of preoperative systemic treatment, optimal dose/duration, and toxicity in patients with CRLM. OUTCOMES: Ninety-eight patients were eligible for analysis. Most patients received oxaliplatin-based chemotherapy (72.7%), while 15.9% received both oxaliplatin and irinotecan. Biologic agents were administered in 48.9% of patients. Overall, chemotherapy-induced liver injury was observed in 38.5%. The median disease-free survival (DFS) and overall survival (OS) were 8.7 months and 3.6 years, respectively. Baseline, pre-surgery, and increased Fong scores after preoperative chemotherapy were significantly associated with DFS and OS. In multivariate analysis, a high Fong score at baseline (p=0.018) was significantly associated with shorter DFS, whereas male sex (p=0.040) and liver surgery (p=0.044) were related to longer OS. CONCLUSION: In our study, Fong clinical risk scores, female sex, and liver surgery as a part of liver-directed therapy were independent prognostic factors for survival in CRLM patients who received preoperative chemotherapy. These clinical factors should be considered as an option to guide physicians' decisions in selecting patients with CRLM who may benefit most from curative liver-directed therapy.

Predicting multi-vascular diseases in patients with coronary artery disease.

Background: Because of its systemic nature, the occurrence of atherosclerosis in the coronary arteries can also indicate a risk for other vascular diseases.  However, screening program targeted for all patients with coronary artery disease (CAD) is highly ineffective and no studies have assessed the risk factors for developing multi-vascular diseases in general. This study constructed a predictive model and scoring system to enable targeted screening for multi-vascular diseases in CAD patients. Methods: This cross-sectional study includes patients with CAD, as diagnosed during coronary angiography or percutaneous coronary intervention from March 2021 to December 2021. Coronary artery stenosis (CAS) and abdominal aortic aneurysm (AAA) were diagnosed using Doppler ultrasound while peripheral artery disease (PAD) was diagnosed based on ABI score. Multivariate logistic regression was conducted to construct the predictive model and risk scores. Validation was conducted using ROC analysis and Hosmer-Lemeshow test. Results: Multivariate analysis showed that ages of >60 years (OR [95% CI] = 1.579 [1.153-2.164]), diabetes mellitus (OR = 1.412 [1.036-1.924]), cerebrovascular disease (OR = 3.656 [2.326-5.747]), and CAD3VD (OR = 1.960 [1.250-3.073]) increased the odds for multi-vascular disease. The model demonstrated good predictive capability (AUC = 0.659) and was well-calibrated (Hosmer-Lemeshow p = 0.379). Targeted screening for high-risk patients reduced the number needed to screen (NNS) from 6 in the general population to 3 and has a high specificity of 96.5% Conclusions: Targeted screening using clinical risk scores was able to decrease NNS with good predictive capability and high specificity.

Clinical Risk Score for Prediction of Urgency in Carotid Cavernous Sinus Fistulas.

Purpose: To develop a clinical risk score for the prediction of urgency in patients with carotid cavernous sinus fistulas (CCFs) and test for the discriminative ability of the diagnostic prediction. Methods: The medical charts of 60 patients with CCFs were retrospectively reviewed. The clinical characteristics of direct and dural CCFs were analyzed by logistic regression. The clinical risk score was developed from the coefficient in the multivariable regression model and used to predict direct CCFs which were more urgent than the dural type. The score prediction was reported as an area under the receiver operating characteristic (AuROC) curve and 95% confidence interval (95% CI). Results: In a univariable analysis, the clinical characteristics which increased the risk of direct CCFs were age, gender, trauma, underlying diseases, visual acuity (VA) at presentation, bruit, chemosis, and dilated retinal vessels. However, in multivariable analysis, the significant predictors were limited to age, trauma, bruit, underlying diseases and logMAR VA. Regression coefficient of each predictor was converted to a risk score and summation of scores from these predictors for each patient was calculated. The total risk score predicted the urgent direct CCFs correctly with AuROC of 97.77% (95% CI; 93.57, 100). Conclusion: The clinical risk score for the prediction of urgent direct CCFs has been developed and used in the patients with CCFs in our setting. The discriminative ability of the score prediction is high. This simple clinical risk score may help clinicians suspect direct CCFs and urgently refer the patients to have prompt angiography and treatment.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.

PURPOSE: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. METHODS: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. RESULTS: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. CONCLUSION: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

Integrating polygenic and clinical risks to improve stroke risk stratification in prospective Chinese cohorts.

The utility of the polygenic risk score (PRS) to identify individuals at higher risk of stroke beyond clinical risk remains unclear, and we clarified this using Chinese population-based prospective cohorts. Cox proportional hazards models were used to estimate the 10-year risk, and Fine and Gray's models were used for hazard ratios (HRs), their 95% confidence intervals (CIs), and the lifetime risk according to PRS and clinical risk categories. A total of 41,006 individuals aged 30-75 years with a mean follow-up of 9.0 years were included. Comparing the top versus bottom 5% of the PRS, the HR was 3.01 (95%CI 2.03-4.45) in the total population, and similar findings were observed within clinical risk strata. Marked gradients in the 10-year and lifetime risk across PRS categories were also found within clinical risk categories. Notably, among individuals with intermediate clinical risk, the 10-year risk for those in the top 5% of the PRS (7.3%, 95%CI 7.1%-7.5%) reached the threshold of high clinical risk (⩾7.0%) for initiating preventive treatment, and this effect of the PRS on refining risk stratification was evident for ischemic stroke. Even among those in the top 10% and 20% of the PRS, the 10-year risk would also exceed this level when aged ⩾50 and ⩾60 years, respectively. Overall, the combination of the PRS with the clinical risk score improved the risk stratification within clinical risk strata and distinguished actual high-risk individuals with intermediate clinical risk.

Pre-transplant Biomarkers of Immune Dysfunction Improve Risk Assessment of Post-transplant Mortality Compared to Conventional Clinical Risk Scores.

Introduction: There is a critical need to accurately stratify liver transplant (LT) candidates' risk of post-LT mortality prior to LT to optimize patient selection and avoid futility. Here, we compare previously described pre-LT clinical risk scores with the recently developed Liver Immune Frailty Index (LIFI) for prediction of post-LT mortality. LIFI measures immune dysregulation based on pre-LT plasma HCV IgG, MMP3 and Fractalkine. LIFI accurately predicts post-LT mortality, with LIFI-low corresponding to 1.4% 1-year post-LT mortality compared with 58.3% for LIFI-high (C-statistic=0.85). Methods: LIFI was compared to MELD, MELD-Na, MELD 3.0, D-MELD, MELD-GRAIL, MELD-GRAIL-Na, UCLA-FRS, BAR, SOFT, P-SOFT, and LDRI scores on 289 LT recipients based on waitlist data at the time of LT. Survival, hazard of early post-LT death, and discrimination power (C-statistic) were assessed. Results: LIFI showed superior discrimination (highest C-statistic) for post-LT mortality when compared to all other risk scores, irrespective of biologic MELD. On univariate analysis, the LIFI showed a significant correlation with mortality 6-months, as well as 1-, 3-, and 5-years. No other pre-LT scoring system significantly correlated with post-LT mortality. On bivariate adjusted analysis, African American race (p<0.05) and pre-LT cardiovascular disease (p=0.053) were associated with early- and long-term post-LT mortality. Patients who died within 1-yr following LT had a significantly higher incidence of infections, including 30-day and 90-day incidence of any infection, pneumonia, abdominal infections, and UTI (p<0.05). Conclusions: LIFI, which measures pre-LT biomarkers of immune dysfunction, more accurately predicts risk of post-LT futility compared with current clinical predictive models. Pre-LT assessment of immune dysregulation may be critical in predicting mortality after LT and may optimize selection of candidates with lowest risk of futile outcomes.

A Telephone Triage System for Patients Calling with Symptoms of a Posterior Vitreous Detachment.

PURPOSE: The purpose of the study was to develop a simple telephone questionnaire, without physical examination input, that predicts which patients calling with symptoms of a posterior vitreous detachment (PVD) have a retinal tear (RT) or rhegmatogenous retinal detachment (RD). DESIGN: Prospective cohort (quality improvement) study. PARTICIPANTS: All patients with symptoms consistent with a PVD calling a major academic ophthalmology department over a 4-month period in 2020 and who were seen on follow-up within 1.5 months (211 screened and 193 included). METHODS: A comprehensive telephone questionnaire assessing for RT/RD risk factors was administered by telephone triage staff to all patients calling with symptoms of flashes, floaters, or curtain/veil in their vision. Multivariable logistic regression was used to determine risk factors most predictive of having an RT/RD during the add-on visit. Risk factor odds ratios were used to develop an RT/RD risk score. MAIN OUTCOMES MEASURES: Development of a clinical risk score for having an RT/RD at the add-on visit after telephone triage. RESULTS: Approximately 55% of patients were previously established in the retina clinic, 26% were new to the department, 19% were previously established in the comprehensive clinic, and 7% had an RT/RD at the add-on visit. Out of 23 questions and 70 prespecified possible answers from the telephone questionnaire, the final clinical risk score for RT/RDs was derived from 7 questions and 15 possible answers. The simplified questionnaire can be administered quickly by telephone operators without any reference to physical examination or the patient's chart. The receive-operator curve for our final multivariable logistic regression and clinical risk score models have an area under the curve of > 0.90. Using a conservative clinical risk score, approximately 50% of all patients without an RT/RD can be safely seen nonurgently. Progressively higher scores can be used to determine relative urgency of an appointment. CONCLUSIONS: To our knowledge, this is the first study to predict risk of an RT/RD in a patient calling with symptoms consistent with a PVD without reference to the patient's physical examination or chart. Our clinical risk scoring system can be used to determine urgency of an add-on appointment and increase the number of low-risk patients with symptomatic PVDs who are scheduled routinely. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Validation of the simple clinical risk score for the early detection of severe dengue in adult patients

Background@#In 2009, the World Health Organization revised the Dengue Fever guidelines to more accurately identify patients at risk of developing severe dengue. Despite these guidelines, early diagnosis of severe dengue remains challenging for clinicians. Several scoring systems have been developed to identify patients at highest risk for severe dengue however; these studies have a study population limited to children and did not include adult patients.@*Objective@#The purpose of this study is to validate the Simple Clinical Risk Score in predicting who will develop severe Dengue among adult patients with Dengue fever.@*Methods@#This is a prospective cohort, single-center, observational study conducted at Silliman University Medical Center from August 2019 to August 2020. A total of 481 laboratory confirmed dengue patients were included and categorized into two models based on the day of illness. Each model used a clinical risk score of 1 point as a cut-off for predicting severe Dengue. Validation was done using the risk-odds ratio and substantiated by the odds ratio, signifying that there is more likely greater association between dengue patients to develop severe dengue.@*Results@#In model 1, a total of 339 patients were analyzed with 6 patients who achieved a score of 1 developed severe Dengue. In model 2, a total of 142 patients were analyzed and 3 patients who achieved a score of 1 developed severe dengue.@*Conclusion@#The simple clinical risk score can assist clinicians in deciding and stratifying dengue patients who need hospitalization not only in resource-limited areas but also during this height of the pandemic. While the findings had a lesser number of participants, it still remained context-specific and is able to demonstrate a predictive ability for severe disease, thereby optimizing informed decisions for hospital admissions in settings with limited laboratory resources.

C5a elevation in convalescents from severe COVID-19 is not associated with early complement activation markers C3bBbP or C4d.

Numerous publications have underlined the link between complement C5a and the clinical course of COVID-19. We previously reported that levels of C5a remain high in the group of severely ill patients up to 90 days after hospital discharge. We have now evaluated which complement pathway fuels the elevated levels of C5a during hospitalization and follow-up. The alternative pathway (AP) activation marker C3bBbP and the soluble fraction of C4d, a footprint of the classical/lectin (CP/LP) pathway, were assessed by immunoenzymatic assay in a total of 188 serial samples from 49 patients infected with SARS-CoV-2. Unlike C5a, neither C3bBbP nor C4d readouts rose proportionally to the severity of the disease. Detailed correlation analyses in hospitalization and follow-up samples collected from patients of different disease severity showed significant positive correlations of AP and CP/LP markers with C5a in certain groups, except for the follow-up samples of the patients who suffered from highly severe COVID-19 and presented the highest C5a readouts. In conclusion, there is not a clear link between persistently high levels of C5a after hospital discharge and markers of upstream complement activation, suggesting the existence of a non-canonical source of C5a in patients with a severe course of COVID-19.

Liver cancer risk-predictive molecular biomarkers specific to clinico-epidemiological contexts.

Hepatocellular carcinoma (HCC) risk prediction is increasingly important because of the low annual HCC incidence in patients with the rapidly emerging non-alcoholic fatty liver disease or cured HCV infection. To date, numerous clinical HCC risk biomarkers and scores have been reported in literature. However, heterogeneity in clinico-epidemiological context, e.g., liver disease etiology, patient race/ethnicity, regional environmental exposure, and lifestyle-related factors, obscure their real clinical utility and applicability. Proper characterization of these factors will help refine HCC risk prediction according to certain clinical context/scenarios and contribute to improved early HCC detection. Molecular factors underlying the clinical heterogeneity encompass various features in host genetics, hepatic and systemic molecular dysregulations, and cross-organ interactions, which may serve as clinical-context-specific biomarkers and/or therapeutic targets. Toward the goal to enable individual-risk-based HCC screening by incorporating the HCC risk biomarkers/scores, their assessment in patient with well-defined clinical context/scenario is critical to gauge their real value and to maximize benefit of the tailored patient management for substantial improvement of the poor HCC prognosis.

Efficacy of risk-stratified indicators for adjuvant chemotherapy with fluorouracil and oxaliplatin after hepatectomy for colorectal cancer liver metastasis.

BACKGROUND: The recurrence rate after hepatectomy for colorectal cancer liver metastasis (CRLM) is high, and there is no consensus regarding the effect of adjuvant chemotherapy (AC) using oxaliplatin (doublet AC) in these patients. METHODS: The present study included 91 patients who underwent hepatectomy for complete resection at our hospitals between 2008 and 2018. Based on whether or not they had undergone doublet AC, patients were divided into AC (n = 35) and non-AC (n = 56) groups. The recurrent risk was evaluated by the Memorial Sloan Kettering Cancer Center clinical risk score (MSKCC-CRS). RESULTS: The number of females and median age were higher in the AC group (51.4% vs 25.0%, p = 0.010 and 67 vs 61 years, p = 0.012, respectively). The median follow-up period was 45 months (range, 6-101 months). Doublet AC was an independent prognostic factor for 5-year relapse-free survival (hazard ratio, 0.225; 95%CI, 0.097-0.522; p < 0.001) and for 5-year overall survival (hazard ratio, 0.165; 95%CI, 0.057-0.476; p < 0.001) in multivariate analysis. In patients with a high risk of recurrence (MSKCC-CRS 3-5), 5-year relapse-free survival and 5-year overall survival was higher in the doublet AC group than in the non-AC group (p < 0.01). In low-risk patients (MSKCC-CRS 0-2), 5-year relapse-free survival and 5-year overall survival were similar between the groups. CONCLUSIONS: Doublet AC could have a positive effect on prognosis after curative resection of CRLM, especially in high-risk patients. The selection of patients and AC regimen should take into consideration the risk of recurrence.

Development of a clinical risk score to predict death in patients with COVID-19.

OBJECTIVE: To build a clinical risk score to aid risk stratification among hospitalised COVID-19 patients. METHODS: The score was built using data of 417 consecutive COVID-19 in patients from Kuwait. Risk factors for COVID-19 mortality were identified by multivariate logistic regressions and assigned weighted points proportional to their beta coefficient values. A final score was obtained for each patient and tested against death to calculate an Receiver-operating characteristic curve. Youden's index was used to determine the cut-off value for death prediction risk. The score was internally validated using another COVID-19 Kuwaiti-patient cohort of 923 patients. External validation was carried out using 178 patients from the Italian CoViDiab cohort. RESULTS: Deceased COVID-19 patients more likely showed glucose levels of 7.0-11.1 mmol/L (34.4%, p < 0.0001) or >11.1 mmol/L (44.3%, p < 0.0001), and comorbidities such as diabetes and hypertension compared to those who survived (39.3% vs. 20.4% [p = 0.0027] and 45.9% vs. 26.6% [p = 0.0036], respectively). The risk factors for in-hospital mortality in the final model were gender, nationality, asthma, and glucose categories (<5.0, 5.5-6.9, 7.0-11.1, or 11.1 > mmol/L). A score of ≥5.5 points predicted death with 75% sensitivity and 86.3% specificity (area under the curve (AUC) 0.901). Internal validation resulted in an AUC of 0.826, and external validation showed an AUC of 0.687. CONCLUSION: This clinical risk score was built with easy-to-collect data and had good probability of predicting in-hospital death among COVID-19 patients.

A polygenic risk score improves risk stratification of coronary artery disease: a large-scale prospective Chinese cohort study.

AIMS: To construct a polygenic risk score (PRS) for coronary artery disease (CAD) and comprehensively evaluate its potential in clinical utility for primary prevention in Chinese populations. METHODS AND RESULTS: Using meta-analytic approach and large genome-wide association results for CAD and CAD-related traits in East Asians, a PRS comprising 540 genetic variants was developed in a training set of 2800 patients with CAD and 2055 controls, and was further assessed for risk stratification for CAD integrating with the guideline-recommended clinical risk score in large prospective cohorts comprising 41 271 individuals. During a mean follow-up of 13.0 years, 1303 incident CAD cases were identified. Individuals with high PRS (the highest 20%) had about three-fold higher risk of CAD than the lowest 20% (hazard ratio 2.91, 95% confidence interval 2.43-3.49), with the lifetime risk of 15.9 and 5.8%, respectively. The addition of PRS to the clinical risk score yielded a modest yet significant improvement in C-statistic (1%) and net reclassification improvement (3.5%). We observed significant gradients in both 10-year and lifetime risk of CAD according to the PRS within each clinical risk strata. Particularly, when integrating high PRS, intermediate clinical risk individuals with uncertain clinical decision for intervention would reach the risk levels (10-year of 4.6 vs. 4.8%, lifetime of 17.9 vs. 16.6%) of high clinical risk individuals with intermediate (20-80%) PRS. CONCLUSION: The PRS could stratify individuals into different trajectories of CAD risk, and further refine risk stratification for CAD within each clinical risk strata, demonstrating a great potential to identify high-risk individuals for targeted intervention in clinical utility.