On User Interaction Behavior as Evidence for Computer Forensic Analysis

The objective was to study the dynamics of B-type natriuretic peptide (BNP) and its relationship with hemodynamic parameters during on-pump coronary artery bypass grafting (CABG), and to evaluate the informativeness of the biomarker as a predictor of myocardial dysfunction.Materials and methods. The study involved 127 patients aged 59 [54–66.75] years with ischemic heart disease who underwent CABG. The BNP blood level was determined in the operating room at stages: I – before surgery (BNP1 ), II – at the end of surgery (BNP2 ). Hemodynamic parameters were analyzed at the same stages. Correlation analysis, logistic regression with the calculation of the odds ratio (OR) and 95% confidence interval (95% CI) and ROC analysis with the calculation of the area under the ROC curve (AUC) were used.Results. BNP1 blood level was 49 [25.6–91.6], BNP2 – 90 [47.8–140.2] pg/ml (p < 0.0001). BNP1 correlated with central venous pressure (CVP) at stage I (rho = 0.212; p = 0.017) and with pulmonary artery wedge pressure (PAWP) at stage II (rho = 0.204; p = 0.045). BNP2 correlated with PAWP at stage II (rho = 0.204; p = 0.045). BNP1 > 52.1 pg/ml was the predictor of ICU length of stay > 24 hours (OR 1.0290, 95% CI 1.0154– 1.0427, p < 0.0001, AUC 0.775), BNP1 > 71 pg/ml was the predictor of inotropic index > 5 c. u. (OR 1.0076, 95% CI 1.0015–1.0138, p = 0.014, AUC 0.705) and BNP1 > 90.8 pg/ml was the predictor of vasoactive inotropic index > 10 c. u. (OR 1.0070, 95% CI 1.0014–1.0126, p = 0.013, AUC 0.727). BNP2 > 67.5 pg/ml was the predictor of ICU length of stay > 24 hours (OR 1.0179, 95% CI 1.0073–1.0287, p < 0.0009, AUC 0.763), BNP2 > 94.3 pg/ml was the predictor of inotropic index > 5 c. u. (OR 1.0063, 95% CI 1.0010–1.0117, p = 0.020, AUC 0.713), BNP2 > 144 pg/ml was the predictor of intra-aortic balloon pumping (OR 1.0037, 95% CI 1 .0000–1.0074, p = 0.048, AUC 0.854), BNP2 > 159 pg/ml was the predictor of vasoactive inotropic index > 10 c. u. (OR 1.0072, 95% CI 1.0006–1.0139, p = 0.033, AUC 0.729) and BNP2 > 161 pg/ml was the predictor of early mortality in the ICU (OR 1.0040, 95% CI 1, 0000-1.0080, p = 0.049, AUC 0.845). Conclusion. In 78.7% of patients undergoing on-pump CABG, BNP blood level does not exceed the upper limit of normal; by the end of surgery, the biomarker level increases by 32.9 [17.7–62.0] pg/ml. Before and at the end of surgery, BNP values are weakly correlated with CVP and PAWP and do not correlate with other hemodynamic parameters. Before surgery, BNP blood level in the range of 52.1–90.8 pg/ml are predictors of ICU stay > 24 hours (AUC 0.775), inotropic scale > 5 (AUC 0.705) and vasoactive-inotropic scale > 10 c. u. (AUC 0.727). At the end of surgery, BNP > 67.5 pg/ml is associated with an ICU stay > 24 hours (AUC 0.763), and BNP > 90.4 pg/ml is associated with inotropic scale > 5 c. u. (AUC 0.713). The BNP, increased to 144.0–161.0 pg/ml, indicates severe myocardial dysfunction, including hemodynamic support with intra-aortic balloon pumping (AUC 0.854), vasoactive-inotropic scale > 10 c. u. (AUC 0.729) and the risk of early mortality in the ICU (AUC 0.845).

Introduction: HDL cholesterol (HDL-C) may not always represent the HDL function. This study was conducted to help predict the risk of coronary artery disease (CAD) in patients with type 2 diabetes (T2D) based on substituting paraoxonase 1 (PON1) as an important enzyme in HDL function for that of HDL-C in the atherogenic ratios LDL-C/HDL-C and log (TG/HDL-C). Methods: A total of 274 subjects undergoing diagnostic coronary angiography in this study; 92 without significant CAD and 182 with severe CAD. The hydrolysis rate of phenylacetate as a PON1 substrate was spectrophotometrically measured in kinetic mode. The area under the ROC curve (AUC) was calculated to quantify the overall discriminative capacity of the variables. Results: ROC analyses showed that LDL-C/PON1 (AUC=0.66, p=0.02) and log (TG/PON1) (AUC=0.65, p=0.019) had higher predictive powers compared with LDL-C/HDL-C (AUC=0.55, p=0.221) and log (TG/HDL-C) (AUC=0.57, p=0.121) for CAD characterization in T2D patients. Also, AUCs of LDL-C/PON1 (AUC=0.74, p=0.014) and log (TG/PON1) (AUC=0.67, p=0.057) were higher than those of LDL-C/HDL-C (AUC=0.51, p=0.469) and log (TG/HDL-C) (AUC=0.53, p=0.364) for predicting CAD in T2D patients with HbA1c ≥7%. The multiple regression analysis with adjustments for gender, age, BMI, duration of T2D, metformin and statin therapy indicated that LDL-C/PON1 [adjusted OR=1.1 (0.99,1.23), p=0.085] and log (TG/PON1) [adjusted OR=3.76 (0.94,14.89), p=0.058] were independently predictors of CAD in T2D patients. Conclusions: New cardiometabolic biomarkers using PON1 provide an incremental predictive value compared with those using HDL-C in identifying subjects at increased risk for CAD among patients with T2D and poor glycemic control. This study sought to exploit the lipoprotein-related biomarkers of CAD from a more effective perspective in T2D. Our findings may help develop clinically cost-effective and functional laboratory measurements to evaluate HDL function.

BackgroundIt is often assumed that rare genetic variants will improve available risk prediction scores. We aimed to estimate the added predictive ability of rare variants for risk prediction of common diseases in hypothetical scenarios.MethodsIn simulated data, we constructed risk models with an area under the ROC curve (AUC) ranging between 0.50 and 0.95, to which we added a single variant representing the cumulative frequency and effect (odds ratio, OR) of multiple rare variants. The frequency of the rare variant ranged between 0.0001 and 0.01 and the OR between 2 and 10. We assessed the resulting AUC, increment in AUC, integrated discrimination improvement (IDI), net reclassification improvement (NRI(>0.01)) and categorical NRI. The analyses were illustrated by a simulation of atrial fibrillation risk prediction based on a published clinical risk model.ResultsWe observed minimal improvement in AUC with the addition of rare variants. All measures increased with the frequency and OR of the variant, but maximum increment in AUC remained below 0.05. Increment in AUC and NRI(>0.01) decreased with higher AUC of the baseline model, whereas IDI remained constant. In the atrial fibrillation example, the maximum increment in AUC was 0.02 for a variant with frequency = 0.01 and OR = 10. IDI and NRI showed at most minimal increase for variants with frequency greater than or equal to 0.005 and OR greater than or equal to 5.ConclusionsSince rare variants are present in only a minority of affected individuals, their predictive ability is generally low at the population level. To improve the predictive ability of clinical risk models for complex diseases, genetic variants must be common and have substantial effect on disease risk.

To evaluate whether the addition of gadolinium-enhanced MRI and diffusion-weighted imaging (DWI) improves T2 sequence performance for the diagnosis of local recurrence (LR) from rectal cancer and to assess which approach is better at formulating this diagnosis among readers with different experience. Forty-three patients with suspected LR underwent pelvic MRI with T2 weighted (T2) sequences, gadolinium fat-suppressed T1 weighted sequences (post-contrast T1), and DWI sequences. Three readers (expert: G, intermediate: E, resident: V) scored the likelihood of LR on T2, T2+post-contrast T1, T2+DWI, and T2+post-contrast T1+DWI. In total, 18/43 patients had LR; on T2 images, the expert reader achieved an area under the ROC curve (AUC) of 0.916, sensitivity of 88.9%, and specificity of 76%; the intermediate reader achieved values of 0.890, 88.9%, and 48%, respectively, and the resident achieved values of 0.852, 88.9%, and 48%, respectively. DWI significantly improved the AUC value for the expert radiologist by up to 0.999 (p=0.04), while post-contrast T1 significantly improved the AUC for the resident by up to 0.950 (p=0.04). For the intermediate reader, both the T2+DWI AUC and T2+post-contrast T1 AUC were better than the T2 AUC (0.976 and 0.980, respectively), but with no statistically significant difference. No statistically significant difference was achieved by any of the three readers by comparing either the T2+DWI AUCs to the T2+post-contrast T1 AUCs or the AUCs of the two pairs of sequences to those of the combined three sequences. Furthermore, using the T2 sequences alone, all of the readers achieved a fair number of "equivocal" cases: they decreased with the addition of either DWI or post-contrast T1 sequences and, for the two less experienced readers, they decreased even more with the three combined sequences. Both DWI and T1 post-contrast MRI increased diagnostic performance for LR diagnosis compared to T2; however, no significant difference was observed by comparing the two different pairs of sequences with the three combined sequences.

The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the case of the full AUC, one can readily construct and optimize convex surrogates by expressing the performance measure as a summation of pairwise terms. The partial AUC, on the other hand, does not admit such a simple decomposable structure, making it more challenging to design and optimize (tight) convex surrogates for this measure. Our approach builds on the structural SVM framework of Joachims ( 2005 ) to design convex surrogates for partial AUC and solves the resulting optimization problem using a cutting plane solver. Unlike the full AUC, where the combinatorial optimization needed in each iteration of the cutting plane solver can be decomposed and solved efficiently, the corresponding problem for the partial AUC is harder to decompose. One of our main contributions is a polynomial time algorithm for solving the combinatorial optimization problem associated with partial AUC. We also develop an approach for optimizing a tighter nonconvex hinge loss-based surrogate for the partial AUC using difference-of-convex programming. Our experiments on a variety of real-world and benchmark tasks confirm the efficacy of the proposed methods.

Radiation Therapy Oncology Group (RTOG) Protocol 0915: A Randomized Phase 2 Study Comparing 2 Stereotactic Body Radiation Therapy (SBRT) Schedules for Medically Inoperable Patients With Stage I Peripheral Non-Small Cell Lung Cancer

Several indicators associated with poor outcomes in older persons have been developed, but a direct comparison of their accuracy is lacking. Knowing which indicator performs better in the prediction of specific outcomes could help health care providers to choose the most suitable one. We compared the accuracy in predicting different clinically-relevant outcomes of five indicators: frailty index (FI), frailty phenotype (FP), the Health Assessment Tool (HAT), walking speed (WS), and multimorbidity. Data from the Swedish National Study on Aging and Care in Kungsholmen, an ongoing population-based study including 3363 people 60+, were used. The ability of the five indicators to predict mortality (3- and 5-year), unplanned hospitalizations (1- and 3-year), and 2+ health provider contacts (6 months prior and after assessment) was compared using the area under the ROC curves (AUC). FI, WS, and HAT showed the best accuracy in the prediction of mortality (AUC for 3-year mortality: 0.84, 0.85, 0.87 respectively; AUC for 5-year mortality: 0.84, 0.85, 0.86 respectively; all p < 0.05). Unplanned hospitalizations were better predicted by the FI (AUC: 1-year 0.73; 3-year 0.72) and HAT (AUC: 1-year 0.73; 3-year 0.71).The most accurate predictor of multiple contacts with health providers was multimorbidity (AUC: 0.67; p < 0.05). All indicators, but multimorbidity, showed higher accuracy among older individuals (75+ years). Different indicators can be used to support physicians during their decision-making process. Some of these tools may also be used to forecast future use of health-care resources, including both hospital-based services and outpatient ones .

Highlights. The article presents a novel and unique method for assessment of left ventricular systolic disfunction using electrocardiography and photoplethysmography data. This method will improve and simplify the detection of cardiovascular diseases.Aim. To evaluate left ventricular (LV) systolic function using electrocardiogram (ECG) and photoplethysmogram (PPG) signals recorded by a single-channel ECG and PPG-based monitor.Methods. The prospective study included 489 patients over 18 years old with various cardiovascular diseases. All participants underwent echocardiography to determine the main indicators of LV systolic function: LV ejection fraction (EF), LV outflow tract velocity time integral (LVOT VTI), and global longitudinal strain (GLS). Moreover, all patients underwent 1-lead ECG and PPG recording using a single-channel ECG and PPG-based monitor (CardioQvark). The obtained data were analyzed, and ROC curve analysis was performed.Results. We have identified ECG and PPG parameters associated with a decrease in LV contractile function. During the analysis, the ECG, T-wave amplitude (TA) and RonsF parameters showed the highest diagnostic accuracy. With EF below 55%, the area under the ROC curve (AUC) was 0.822, sensitivity (Se) 80%, specificity (Sp) 69% in EF below 55% in TA; in RonsF AUC was 0.743, Se 81%, Sp 77%. With EF below 40%, AUC was 0.915, Se 85%, Sp 83% in TA, and in RonsF AUC was 0.844, Se 82%, Sp 82%. Diagnostic accuracy of ECG signals in case of LVOT VTI lower than 16 cm was measured: TA (AUC 0.755, Se 82%, Sp 70%), RonsF (AUC 0.620, Se 77%, Sp 72%). PPG signals were not significantly associated with reduced EF; however, the pulse wave parameters were associated with lower LVOT VTI: in DP-B0 AUC was 0.687, Se 71%, Sp 74%. The combination of ECG and PPG signals was significantly associated with EF below 40% (RonsF * DP-SEP (AUC 0.877, Se 86%, Sp 85%). ECG and PPG signals were not associated with LV GLS.Conclusion. Assessment of LV systolic function can be performed by analyzing ECG and PPG signals recorded using a portable single-channel CardioQvark monitor.

Objective: Human obesity can significantly contribute to hypertension risk, often referred to as the “silent killer”, although targeting whom with weight gain becomes clinically hypertensive is unclear. Whilst therapeutic intervention such as bariatric surgery can mitigate both obesity and hypertension, it is not the most suitable solution for all patients. Therefore an early biomarker detection system to assess hypertension in subjects with weight gain could reduce mortality risk. Such a platform could be developed through a urine centred biomarker system identifying disease related signatures based upon volatile organic compounds (VOCs). The focus of this study was to determine whether urinary VOCs could be used as a potential monitoring tool for detection of blood pressure change in obese- hypertensive bariatric patients. Design and Method: Pre-surgery urine of 23 obese female patients undergoing bariatric surgery was analysed by Field-Asymmetric Ion Mobility Spectrometer (FAIMS). The data was processed by dividing the cohort into two classifications (high and low) based on individual median values: systolic and diastolic blood pressure, mean arterial pressure (MAP), C-reactive protein (CRP) and heart rate. Support Vector Machine (SVM) classifiers were trained with features selected using the Wilcoxon rank sum test. Performance was assessed using 10-fold cross-validation and the area under the ROC curve (AUC). Results: FAIMS urinary VOC analysis highlighted a significant association with blood pressure markers as determined by systolic (ROC curve AUC: 0.918, p < 0.001), MAP (AUC: 0.845 p < 0.001), CRP (AUC: 0.932, p < 0.001) and heart rate (AUC: 0.945, p < 0.001); whilst diastolic did not attain significance (AUC: 0., p = N.S). Additionally the VOCs have predicted change in blood pressure factors 6 months post surgery. Conclusions: This study suggests that urinary VOCs can be used for monitoring change in blood pressure in bariatric patients, additionally this technique could be utilised as a novel-screening tool for prediction of hypertension risk in subjects.

BackgroundIn the global aging population, the incidence of postoperative delirium (POD) is increasing. Therefore, finding its effective predictive tools becomes crucial. We aimed to identify potential Cerebrospinal fluid (CSF)biomarkers for POD.MethodsA total of 825 patients undergoing knee/hip arthroplasty under combined spinal-epidural anesthesia were selected. The patients were aged 40 to 90 years with American Society of Anesthesiologists physical status I~II. The Mini-Mental State Examination was completed 1 day before the operation. CSF was extracted after successful spinal-epidural combined puncture, and α-synuclein (α-syn), amyloid beta40 (Aβ40), amyloid beta42 (Aβ42), t-Tau, phosphorylated Tau (p-Tau), progranulin (PGRN) and soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in the CSF were measured by enzyme-linked immunosorbent assays (ELISA). The patient’s operation time, anesthesia time, intraoperative blood loss and fluid input were also recorded. After the operation, the occurrence rate and severity of POD were determined by the Confusion Assessment Method and the Memorial Delirium Assessment Scale (MDAS), respectively. Patients were categorized into POD group and non-POD group. Logistic regression analysis was performed on the indicators with statistically significant differences, and the area under the ROC curve (AUC) was used to estimate the predictive accuracy of the biomarkers for POD.ResultsA total of 92 patients developed POD and the incidence of POD was 11.15%. The results of the multivariable logistic regression showed that CSF t-Tau (P = 0.004, OR = 1.006, 95%CI 1.002~1.009) and α-syn (P = 0.004, OR = 1.001, 95%CI 1.000~1.001) were positively associated with the occurrence rate of POD, while Aβ42 (P < 0.001, OR = 0.989, 95%CI 0.986~0.993), CSF PGRN (P = 0.002, OR = 0.999, 95%CI 0.999~1.000), Aβ42/ t-Tau (P < 0.001, OR = 0.181, 95%CI 0.102~0.319) and Aβ42/p-Tau (P < 0.001, OR = 0.617, 95%CI 0.526~0.725) were inversely proportional to the occurrence of POD. ROC curve analysis indicated that Aβ42/t-Tau (AUC = 0.823), CSF Aβ42 (AUC = 0.813), Aβ42/p-Tau (AUC = 0.810), α-syn (AUC = 0.644) and PGRN (AUC = 0.638) could predict the occurrence rate of POD. The combination of all these biomarkers showed a greater AUC(0.896) than using any of them alone.ConclusionsCSF Aβ42, PGRN, α-syn, Aβ42/t-Tau and Aβ42/p-Tau might be associated with the occurrence rate of POD in patients undergoing knee/hip arthroplasty.Trial registrationClinical Registration No. ChiCTR2000033439.

Total prostate-specific antigen (tPSA) is flawed for prostate cancer (PCa) detection. [-2]proprostate-specific antigen (p2PSA), a molecular isoform of free PSA (fPSA), shows higher specificity compared with tPSA or percentage of free PSA (%fPSA). The prostate health index (Phi), a measure based on p2PSA and calculated as p2PSA/fPSA × √tPSA, was evaluated in a multicenter study for detecting PCa. A total of 1362 patients from 4 different study sites who had tPSA values of 1.6-8.0 μg/L (668 patients with PCa, 694 without PCa) underwent ≥10 core biopsies. Serum concentrations of tPSA, fPSA (both calibrated against a WHO reference material), and p2PSA were measured on Access2 or DxI800 analyzers (Beckman Coulter). The percentage ratio of p2PSA to fPSA (%p2PSA) and Phi were significantly higher in all PCa subcohorts (positive initial or repeat biopsy result or negative digital rectal examination) (P < 0.0001) compared with patients without PCa. Phi had the largest area under the ROC curve (AUC) (AUC = 0.74) and provided significantly better clinical performance for predicting PCa compared with %p2PSA (AUC = 0.72, P = 0.018), p2PSA (AUC = 0.63, P < 0.0001), %fPSA (AUC = 0.61) or tPSA (AUC = 0.56). Significantly higher median values of Phi were observed for patients with a Gleason score ≥7 (Phi = 60) compared with a Gleason score <7 (Phi = 53; P = 0.0018). The proportion of aggressive PCa (Gleason score ≥7) increased with the Phi score. The results of this multicenter study show that Phi, compared with tPSA or %fPSA, demonstrated superior clinical performance in detecting PCa at tPSA 1.6-8.0 μg/L (i.e., approximately 2-10 μg/L in traditional calibration) and is better able to detect aggressive PCa.

11520 Background: Diffusion-weighted (DW) MRI is a non-contrast technique that can reflect treatment-induced alterations in tumor microstructure and cellularity. ACRIN 6698 was performed as a sub-study of the I-SPY 2 TRIAL to evaluate quantitative DW MRI for early assessment of breast cancer response to neoadjuvant chemotherapy (NAC) in a multisite, multiplatform trial. Methods: The IRB-approved trial was performed at ten institutions. Of 406 enrolled breast cancer patients, 272 were randomized to treatment (12 weekly cycles paclitaxel+/-experimental agent, followed by AC) and underwent breast DW MRI at four time points: pre-NAC (T1), early-NAC after 3 cycles paclitaxel (T2), mid-NAC between paclitaxel and AC (T3) and post-NAC (T4). Tumor apparent diffusion coefficient (ADC) was measured at each time point and compared for patients with and without complete pathologic response (pCR) by Wilcoxon signed rank test. Exploratory analyses were performed across subtypes defined by hormone receptor (HR) and HER2 expression. Performance for predicting pCR was assessed by calculating the area under the ROC curve (AUC). Results: Of 272 treated patients, 227 comprised the final cohort (14 were excluded for missing MRI exams, 31 for poor image quality). Median patient age was 48 (range, 25-77) years, and 71/227 (31.3%) achieved pCR. Subtype groups were HR+/HER2+ (n = 38), HR+/HER2- (n = 95), HR-/HER2+ (n = 20), and HR-/HER2- (n = 74). For the full cohort (all subtypes and treatments), both ADC and change in ADC from T1 were significantly predictive of pCR at T3 (AUC = 0.63, 95% CI 0.55-0.71; AUC = 0.62, 95% CI 0.53-0.70, respectively), and also at T4. ADC measures were not predictive of pCR at either T1or T2. Stratifying by subtype showed change in ADC at T3 was more predictive in HR-/HER2+ (AUC = 0.86) and HR+/HER2- (AUC = 0.75) tumors than HR-/HER2- and HR+/HER2+ tumors (AUC = 0.59 and 0.56, respectively). Conclusions: DW MRI reflects cytotoxic effects of chemotherapy, and mid-treatment ADC was a predictive marker of pCR. The predictive value of ADC varied across biologic subtypes. Further work is needed to determine the comparative predictive value of ADC to other imaging metrics. Clinical trial information: NCT01564368.

The use of machine learning models in consequential decision making often exacerbates societal inequity, in particular yielding disparate impact on members of marginalized groups defined by race and gender. The area under the ROC curve (AUC) is widely used to evaluate the performance of a scoring function in machine learning, but is studied in algorithmic fairness less than other performance metrics. Due to the pairwise nature of the AUC, defining an AUC-based group fairness metric is pairwise-dependent and may involve both intra-group and inter-group AUCs. Importantly, considering only one category of AUCs is not sufficient to mitigate unfairness in AUC optimization. In this paper, we propose a minimax learning and bias mitigation framework that incorporates both intra-group and inter-group AUCs while maintaining utility. Based on this Rawlsian framework, we design an efficient stochastic optimization algorithm and prove its convergence to the minimum group-level AUC. We conduct numerical experiments on both synthetic and real-world datasets to validate the effectiveness of the minimax framework and the proposed optimization algorithm.

The association between white blood cell (WBC) counts and mortality in patients with intracerebral hemorrhage (ICH) has not been established. The aim of this study is to determine whether higher WBC is associated with mortality at 90days. A retrospective observational study was conducted at two medical hospitals in China. Baseline WBC count on admission served as the primary predictor variable. Longitudinal WBC counts within the first week after admission were collected to assess the effects of WBC trajectory and the median and maximum WBC counts on outcomes following ICH. Associations of WBC count with outcomes were evaluated in multivariable regression analyses. We identified 3613 patients with ICH who met the inclusion criteria. After adjusting primary confounding variables, patients with increased WBC count had a significantly higher risk of 90-day mortality (p < 0.001 for trend). In the receiver operating characteristic analyses, the capacity for all-cause mortality prediction by WBC count on admission (area under the ROC curve (AUC) = 0.65) was superior to other important inflammatory markers, includingneutrophil(AUC = 0.64) , lymphocyte(AUC = 0.57), albumin(AUC = 0.57), and platelet count(AUC = 0.53), p < 0.001for WBC vs.neutrophil, and the median WBC count (AUC = 0.66) within the first week after admission was a better marker than admission WBC count (p = 0.02). In patients with ICH, WBC count on admission was associated with all-cause mortality at 90days. Additionally, the median and maximum WBC counts within the first week after admission showed better predictive ability for the 90-day mortality compared with the WBC count on admission.

To determine the most appropriate nutritional assessment tool for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI from four nutritional assessment tools including PNI, GNRI, CONUT, and BMI. Consecutive cases diagnosed with acute coronary syndrome (ACS) and underwent percutaneous coronary intervention (PCI) in the Department of Cardiovascular Medicine of the Air force characteristic medical center from 1 January 2020 to 1 April 2022 were retrospectively collected. The basic clinical characteristics and relevant test and examination indexes were collected uniformly, and the cases were divided into the MACE group (174 cases) and the non-MACE group (372 cases) according to whether a major adverse cardiovascular event (MACE) had occurred within 1 year. Predictive models were constructed to assess the nutritional status of patients with the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling nutritional status (CONUT) scores, and Body Mass Index (BMI), respectively, and to analyze their relationship with prognosis. The incremental value of the four nutritional assessment tools in predicting risk was compared using the Integrated Discriminant Improvement (IDI) and the net reclassification improvement (NRI). The predictive effect of each model on the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI was assessed using area under the ROC curve (AUC), calibration curves, decision analysis curves, and clinical impact curves; comparative analyses were performed. Among the four nutritional assessment tools, the area under the curve (AUC) was significantly higher for the PNI (AUC: 0.798, 95%CI 0.755–0.840 P < 0.001) and GNRI (AUC: 0.760, 95%CI 0.715–0.804 P < 0.001) than for the CONUT (AUC: 0.719,95%CI 0.673–0.765 P < 0.001) and BMI (AUC: 0.576, 95%CI 0.522–0.630 P < 0.001). The positive predictive value (PPV) of PNI: 67.67% was better than GNRI, CONUT, and BMI, and the negative predictive value (NPV): of 83.90% was better than CONUT and BMI and similar to the NPV of GNRI. The PNI, GNRI, and CONUT were compared with BMI, respectively. The PNI had the most significant improvement in the Integrated Discriminant Improvement Index (IDI) (IDI: 0.1732, P < 0.001); the PNI also had the most significant improvement in the Net Reclassification Index (NRI) (NRI: 0.8185, P < 0.001). In addition, of the four nutritional assessment tools used in this study, the PNI was more appropriate for predicting the occurrence of major adverse cardiovascular events (MACE) within 1 year in elderly ACS patients undergoing PCI.