Assessment of outcomes in postaneurysmal subarachnoid bleed patients admitted to the intensive care unit utilizing the subarachnoid haemorrhage international trialist clinicoradiological prediction model for dichotomised functional outcome and mortality

Neuroinflammation plays an essential role in the pathogenesis of acute ischemic stroke (AIS). This study aims to investigate the predictive value of the systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI) on mortality and functional limitation in patients with critical AIS. Patients with critical AIS in a tertiary hospital's intensive care unit (ICU) between June 2020 and 2022 were retrospectively examined. Patients were classified according to their 28-day mortality (survivor and non-survivor group) and functional status (poor and good functional outcomes). The performances of SII and SIRI in predicting mortality and functional outcomes were compared. A total of 198 patients were included in the study. The median age of the entire population was 70 (56-86) years, and 52% (n = 103) were male. Coronary vascular disease/heart failure was found to be significantly higher in the mortality group (p = 0.025). While SII was found to be significantly higher in the mortality group (1,180 vs. 811, p = 0.038), SIRI did not show a significant difference (1.82 vs. 1.70, p = 0.257). SII and SIRI were significantly higher in the poor functional outcome group (p < 0.001 and p = 0.015). In the ROC analysis of the functional status prediction performances of SII and SIRI, the cut-off value of SII was ≥ 1,146, the area under the curve (AUC) = 0.645 (0.568-0.722), the cut-off value of SIRI was ≤ 2.54, AUC = 0.600 (0.520-0.680) was detected. SII helps predict 28-day mortality in patients with critical AIS. Both SII and SIRI can predict functional status at discharge.

Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Science, Medicine, and the Anesthesiologist

MRI-based risk factors for intensive care unit admissions in acute neck infections.

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.

Decision letter: Early prediction of level-of-care requirements in patients with COVID-19

Are Unselected Risk Scores in the Cardiac Intensive Care Unit Needed?

IntroductionThe aim of this study was to evaluate the usefulness of the APACHE II (Acute Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score II) and SOFA (Sequential Organ Failure Assessment) scores compared to simpler models based on age and Glasgow Coma Scale (GCS) in predicting long-term outcome of patients with moderate-to-severe traumatic brain injury (TBI) treated in the intensive care unit (ICU).MethodsA national ICU database was screened for eligible TBI patients (age over 15 years, GCS 3–13) admitted in 2003–2012. Logistic regression was used for customization of APACHE II, SAPS II and SOFA score-based models for six-month mortality prediction. These models were compared to an adjusted SOFA-based model (including age) and a reference model (age and GCS). Internal validation was performed by a randomized split-sample technique. Prognostic performance was determined by assessing discrimination, calibration and precision.ResultsIn total, 1,625 patients were included. The overall six-month mortality was 33%. The APACHE II and SAPS II-based models showed good discrimination (area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.75 to 0.82; and 0.80, 95% CI 0.77 to 0.83, respectively), calibration (P > 0.05) and precision (Brier score 0.166 to 0.167). The SOFA-based model showed poor discrimination (AUC 0.68, 95% CI 0.64 to 0.72) and precision (Brier score 0.201) but good calibration (P > 0.05). The AUC of the SOFA-based model was significantly improved after the insertion of age and GCS (∆AUC +0.11, P < 0.001). The performance of the reference model was comparable to the APACHE II and SAPS II in terms of discrimination (AUC 0.77; compared to APACHE II, ΔAUC −0.02, P = 0.425; compared to SAPS II, ΔAUC −0.03, P = 0.218), calibration (P > 0.05) and precision (Brier score 0.181).ConclusionsA simple prognostic model, based only on age and GCS, displayed a fairly good prognostic performance in predicting six-month mortality of ICU-treated patients with TBI. The use of the more complex scoring systems APACHE II, SAPS II and SOFA added little to the prognostic performance.

A human immunodeficiency virus (HIV) risk assessment tool for pregnant women could identify women who would most benefit from preexposure prophylaxis (PrEP) while minimizing unnecessary PrEP exposure. Data from a prospective study of incident HIV among pregnant/postpartum women in Kenya were randomly divided into derivation (n = 654) and validation (n = 650) cohorts. A risk score was derived using multivariate Cox proportional hazards models and standard clinical prediction rules. Ability of the tool to predict maternal HIV acquisition was assessed using the area under the curve (AUC) and Brier score. The final risk score included the following predictors: having a male partner with unknown HIV status, number of lifetime sexual partners, syphilis, bacterial vaginosis (BV), and vaginal candidiasis. In the derivation cohort, AUC was 0.84 (95% confidence interval [CI], .72-.95) and each point increment in score was associated with a 52% (hazard ratio [HR], 1.52 [95% CI, 1.32-1.76]; P < .001) increase in HIV risk; the Brier score was 0.11. In the validation cohort, the score had similar AUC, Brier score, and estimated HRs. A simplified score that excluded BV and candidiasis yielded an AUC of 0.76 (95% CI, .67-.85); HIV incidence was higher among women with risk scores >6 than with scores ≤6 (7.3 vs 1.1 per 100 person-years, respectively; P < .001). Women with simplified scores >6 accounted for 16% of the population but 56% of HIV acquisitions. A combination of indicators routinely assessed in antenatal clinics was predictive of HIV risk and could be used to prioritize pregnant women for PrEP.

Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept.

Preoperative and ICU Scoring Models for Predicting the In-Hospital Mortality of Patients With Ruptured Abdominal Aortic Aneurysms

PD15-04 ACCURACY OF ACS NSQIP® UNIVERSAL SURGICAL RISK CALCULATOR IN PREDICTING COMPLICATIONS FOLLOWING ROBOT-ASSISTED RADICAL CYSTECTOMY AT A NATIONAL COMPREHENSIVE CANCER CENTER

The Sport Concussion Assessment Tool (SCAT) could be improved by identifying critical subsets that maximize diagnostic accuracy and eliminate low information elements. To identify optimal SCAT subsets for acute concussion assessment. Using Concussion Assessment, Research, and Education (CARE) Consortium data, we compared student-athletes' and cadets' preinjury baselines (n=2178) with postinjury assessments within 6 h (n=1456) and 24 to 48 h (n=2394) by considering demographics, symptoms, Standard Assessment of Concussion (SAC), and Balance Error Scoring System (BESS) scores. We divided data into training/testing (60%/40%) sets. Using training data, we integrated logistic regression with an engineering methodology-mixed integer programming-to optimize models with≤4, 8, 12, and 16 variables (Opt-k). We also created models including only raw scores (Opt-RS-k) and symptom, SAC, and BESS composite scores (summary scores). We evaluated models using testing data. At <6h and 24 to 48h, most Opt-k and Opt-RS-k models included the following symptoms: do not feel right, headache, dizziness, sensitivity to noise, and whether physical or mental activity worsens symptoms. Opt-k models included SAC concentration and delayed recall change scores. Opt-k models had lower Brier scores (BS) and greater area under the curve (AUC) (<6 h: BS=0.072-0.089, AUC=0.95-0.96; 24-48 h: BS=0.085-0.093, AUC=0.94-0.95) than Opt-RS-k (<6 h: BS=0.082-0.087, AUC=0.93-0.95; 24-48 h: BS=0.095-0.099, AUC=0.92-0.93) and summary score models (<6 h: BS=0.14, AUC=0.89; 24-48 h: BS=0.15, AUC=0.87). We identified SCAT subsets that accurately assess acute concussion and improve administration time over the complete battery, highlighting the importance of eliminating "noisy" elements. These findings can direct clinicians to the SCAT components that are most sensitive to acute concussion.

Introduction: There is paucity of literature about the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) surgical risk calculator for prediction of outcomes after robot-assisted radical cystectomy (RARC). We sought to evaluate the accuracy of the ACS NSQIP surgical risk calculator in the patients who underwent RARC at our institute. Methods: We retrospectively reviewed our prospectively maintained database for patients who underwent RARC between 2005 and 2017. Accuracy of the ACS NSQIP surgical risk calculator was assessed, by comparing the rate of actual complication events after surgery with the receiver operating characteristics curve analysis by calculating the fractional area under the curve (AUC) and the Brier score (BS). We utilized the code number 51595 and 51596 in the ACS NSQIP calculator for the patients undergoing radical cystectomy and reconstructed with the ileal conduit and neobladder, respectively. Results: A total of 462 patients were included in this study: 99 (22%) had diabetes, 302 (66%) had hypertension requiring medication, and 241 (52%) were classified as high American Society of Anesthesiologists (≥3) class. The actual observed rates of any complication and serious complications were 48% and 11%, vs 29% and 25% predicted by the ACS NSQIP, respectively. The actual mean length of hospital stay (10.6 ± 7.8 days) was longer compared with the predicted length (8.5 ± 1.6 days). AUC values were low and the BSs were high for any complication (AUC: 0.50 and BS: 0.29), serious complication (AUC: 0.53 and BS: 0.12), urinary tract infection (AUC: 0.61 and BS: 0.14), renal insufficiency (AUC: 0.64 and BS: 0.08), return to operation room (AUC: 0.58 and BS: 0.07), and early readmission (AUC: 0.55 and BS: 0.11, respectively). Conclusions: The ACS NSQIP calculator demonstrated low accuracy in predicting postoperative outcomes after RARC. These findings highlight the need for development of procedure- and technique-specific RARC calculators.

Background: Endocrine alterations of the hypothalamic-pituitary-axis are one of the first measurable physiological changes in cerebral insults. During acute stress, human growth hormone (GH) is stimulated and has shown to have a prognostic value in various diseases. Within this pilot study, we evaluated the prognostic value of GH in patients with acute intracerebral hemorrhage (ICH).Methods: In a prospective observational study in 40 consecutive patients with ICH, GH was measured on admission. The prognostic value of GH to predict 30-day mortality and 90-day functional outcome was assessed. Favorable functional outcome was defined as Barthel Index score >85 points and Modified Rankin Scale <3 points.Results: GH levels were increased in patients who died within 30 days as compared to survivors (0.45 (IQR 0.20–1.51) vs. 1.51 (IQR 0.91–4.08) p = 0.03), and in patients with an unfavorable functional outcome as compared to patients with a favorable functional outcome after 90 days 0.28 (IQR 0.16–0.61) vs. 0.78 (IQR 0.31–1.99) p = 0.03). For mortality prediction, receiver-operating-characteristics revealed an area under the curve (AUC) on admission for GH of 0.78 (95% CI 0.60–0.96), which was in the range of the Glasgow Coma Score (GCS) (AUC 0.82 (95% CI 0.59–1.00) p = 0.80). For functional outcome prediction, GH had an AUC of 0.71 (95% CI 0.54–0.87), which was statistically not different from the GCS (AUC 0.81 (95% CI 0.68–0.94) p = 0.36).Conclusions: In our small cohort of patients with acute ICH, elevated GH level were associated with increased mortality and worse outcome. If confirmed in a larger study, GH levels may be used as an additional prognostic factor in ICH patients. (ClincalTrials.gov number NCT00390962).