The significance of inflammatory markers in prognosticating the effectiveness and safety of immunotherapy in conjunction with chemotherapy during the primary intervention of advanced non-small cell lung carcinoma

OBJECTIVE

The aim of this study is to scrutinize the prognostic significance of inflammatory biomarkers concerning the effectiveness and safety of combining PD-1 inhibition with chemotherapy in the management of advanced NSCLC.

METHODS

We conducted a retrospective analysis involving 206 NSCLC patients who received treatment at Qingdao Municipal Hospital. The study encompassed the acquisition of baseline clinical attributes and hematological parameters of these patients. The optimal threshold values for PLT and NLR were ascertained based on pre-treatment evaluations, with a particular focus on their association with PFS. Variables linked to PFS were subject to scrutiny through Kaplan-Meier analysis and logistic regression. The Receiver Operating Characteristic (ROC) curve served as the means to determine the ideal cut-off values for categorizing levels of inflammatory markers into high and low classifications. We employed Chi-square tests to evaluate the relationship between elevated and reduced baseline levels of inflammatory markers and irAE.

RESULTS

Kaplan-Meier analysis disclosed that patients in the low baseline PLT group and the low NLR group exhibited a substantially more favorable prognosis in contrast to their counterparts in the high baseline PLT and high NLR groups. Multivariate analysis indicated that diminished baseline PLT and NLR levels before treatment independently foretell extended PFS. Chi-square analysis underscored a substantial correlation between baseline WBC, NEUT, LYMPH, MONO, and NLR levels and irAE.

CONCLUSION

Subdued baseline PLT and NLR levels may serve as indicators of a more auspicious prognosis in patients contending with advanced NSCLC undergoing the combination of PD-1 inhibition and chemotherapy. Elevated baseline levels of inflammatory markers antedating PD-1 therapy in advanced NSCLC may be intimately interrelated with the occurrence of irAE.

Clinicians' approach to predicting post-cardiac arrest outcomes for patients enrolled in a United States clinical trial

PURPOSE

Perceived poor prognosis can lead to withdrawal of life-sustaining therapies (WLST) in patients who might otherwise recover. We characterized clinicians' approach to post-arrest prognostication in a multicenter clinical trial.

METHODS

Semi-structured interviews were conducted with clinicians who treated a comatose post-cardiac arrest patient enrolled in the Influence of Cooling Duration on Efficacy in Cardiac Arrest Patients (ICECAP) trial (NCT04217551). Two authors independently analyzed each interview using inductive and deductive coding. The clinician reported how they arrived at a prognosis for the specific patient. We summarized the frequency with which clinicians reported using objective diagnostics to formulate their prognosis, and compared the reported approaches to established guidelines. Each respondent provided demographic information and described local neuroprognostication practices.

RESULTS

We interviewed 30 clinicians at 19 US hospitals. Most claimed adherence to local hospital neuroprognostication protocols (n = 19). Prognostication led to WLST for perceived poor neurological prognosis in 15/30 patients, of whom most showed inconsistencies with guidelines or trial recommendations, respectively. In 10/15 WLST cases, clinicians reported relying on multimodal testing. A prevalent theme was the use of "clinical gestalt," defined as prognosticating based on a patient's overall appearance or a subjective impression in the absence of objective data. Many clinicians (21/30) reported using clinical gestalt for initial prognostication, with 9/21 expressing high confidence initially.

CONCLUSION

Clinicians in our study state they follow neuroprognostication guidelines in general but often do not do so in actual practice. They reported clinical gestalt frequently informed early, highly confident prognostic judgments, and few objective tests changed initial impressions. Subjective prognostication may undermine well-designed trials.

Effect of immune checkpoint inhibitors at different treatment time periods on prognosis of patients with extensive-stage small-cell lung cancer

BACKGROUND

The application of immune checkpoint inhibitors (ICIs) in treating patients with extensive-stage small-cell lung cancer (ES-SCLC) has brought us new hope, but the real-world outcome is relatively lacking. Our aim was to investigate the clinical use, efficacy, and survival benefit of ICIs in ES-SCLC from real-world data analysis.

METHODS

A retrospective analysis of ES-SCLC patients was conducted between 2012 and 2022. Progression-free survival (PFS) and overall survival (OS) were assessed between groups to evaluate the value of ICIs at different lines of treatment. PFS1 was defined as the duration from initial therapy to disease progression or death. PFS2 was defined as the duration from the first disease progression to the second disease progression or death.

RESULTS

One hundred and eighty patients with ES-SCLC were included. We performed landmark analysis, which showed that compared to the second-line and subsequent-lines ICIs-combined therapy group (2SL-ICIs) and non-ICIs group, the first-line ICIs-combined therapy group (1L-ICIs) prolonged OS and PFS1. There was a trend toward prolonged OS in the 2SL-ICIs group than in the non-ICIs group, but the significance threshold was not met (median OS 11.94 months vs. 11.10 months, P = 0.14). A longer PFS2 was present in the 2SL-ICIs group than in the non-ICIs group (median PFS2 4.13 months vs. 2.60 months, P < 0.001).

CONCLUSION

First-line ICIs plus chemotherapy should be applied in clinical practice. If patients did not use ICIs plus chemotherapy in first-line therapy, the use of ICIs in the second line or subsequent lines of treatment could prolong PFS2.

Prognostic value of cognitive impairment, assessed by the Clock Drawing Test, in emergency department patients presenting with non-specific complaints

BACKGROUND

Cognitive impairment (CI) is common among older patients presenting to the emergency department (ED). The failure to recognize CI at ED presentation constitutes a high risk of additional morbidity, mortality, and functional decline. The Clock Drawing Test (CDT) is a well-established cognitive screening test.

AIM

In patients presenting to the ED with non-specific complaints (NSCs), we aimed to investigate the usability of the CDT and its prognostic value regarding length of hospital stay (LOS) and mortality.

METHOD

Secondary analysis of the Basel Non-specific Complaints (BANC) trial, a prospective delayed type cross-sectional study with a 30-day follow-up. In three EDs, patients presenting with NSCs were enrolled. The CDT was administered at enrollment.

RESULTS

In the 1,278 patients enrolled, median age was 81 [74, 87] years and 782 were female (61.19%). A valid CDT was obtained in 737 (57.7%) patients. In patients without a valid CDT median LOS was higher (29 [9, 49] days vs. 22 [9, 45] days), and 30-day mortality was significantly higher than in patients with a valid CDT (n = 45 (8.32%) vs. n = 39 (5.29%)). Of all valid CDTs, 154 clocks (20.9%) were classified as normal, 55 (7.5%) as mildly deficient, 297 (40.3%) as moderately deficient, and 231 (31.3%) as severely deficient. Mortality and LOS increased along with the CDT deficits (p = 0.012 for 30-day mortality; p < 0.001 for LOS).

CONCLUSION

The early identification of patients with CI may lead to improved patient management and resource allocation. The CDT could be used as a risk stratification tool for older ED patients presenting with NSCs, as it is a predictor for 30-day mortality and LOS.

Plasma glial fibrillary acidic protein and tau: predictors of neurological outcome after cardiac arrest

BACKGROUND

The purpose was to evaluate glial fibrillary acidic protein (GFAP) and total-tau in plasma as predictors of poor neurological outcome after out-of-hospital (OHCA) and in-hospital cardiac arrest (IHCA), including comparisons with neurofilament light (NFL) and neuron-specific enolase (NSE).

METHODS

Retrospective multicentre observational study of patients admitted to an intensive care unit (ICU) in three hospitals in Sweden 2014-2018. Blood samples were collected at ICU admission, 12 h, and 48 h post-cardiac arrest. Poor neurological outcome was defined as Cerebral Performance Category 3-5 at 2-6 months after cardiac arrest. Plasma samples were retrospectively analysed for GFAP, tau, and NFL. Serum NSE was analysed in clinical care. Prognostic performances were tested with the area under the receiver operating characteristics curve (AUC).

RESULTS

Of the 428 included patients, 328 were OHCA, and 100 were IHCA. At ICU admission, 12 h and 48 h post-cardiac arrest, GFAP predicted neurological outcome after OHCA with AUC (95% CI) 0.76 (0.70-0.82), 0.86 (0.81-0.90) and 0.91 (0.87-0.96), and after IHCA with AUC (95% CI) 0.77 (0.66-0.87), 0.83 (0.74-0.92) and 0.83 (0.71-0.95). At the same time points, tau predicted outcome after OHCA with AUC (95% CI) 0.72 (0.66-0.79), 0.75 (0.69-0.81), and 0.93 (0.89-0.96) and after IHCA with AUC (95% CI) 0.61 (0.49-0.74), 0.68 (0.56-0.79), and 0.77 (0.65-0.90). Adding the change in biomarker levels between time points did not improve predictive accuracy compared to the last time point. In a subset of patients, GFAP at 12 h and 48 h, as well as tau at 48 h, offered similar predictive value as NSE at 48 h (the earliest time point NSE is recommended in guidelines) after both OHCA and IHCA. The predictive performance of NFL was similar or superior to GFAP and tau at all time points after OHCA and IHCA.

CONCLUSION

GFAP and tau are promising biomarkers for neuroprognostication, with the highest predictive performance at 48 h after OHCA, but not superior to NFL. The predictive ability of GFAP may be sufficiently high for clinical use at 12 h after cardiac arrest.

Low skeletal muscle mass and treatment outcomes among adults with haematologic malignancies: A systematic review and meta-analysis

BACKGROUND

Low skeletal muscle mass (LSMM) and/or, function associated with an increased risk of treatment-related toxicities and inferior overall survival (OS) among adults with solid malignancies. However, the association between LSMM and treatment-related toxicities among adults with haematologic malignancies remains unclear.

METHODS

Using a pre-published protocol (CRD42020197814), we searched seven bibliographic databases from inception to 08/2021 for studies reporting the impact of LSMM among adults ≥18 years with a known haematologic malignancy. The primary outcome of interest was OS, and secondary outcomes included progression free survival (PFS) and non-relapse mortality (NRM). These effect sizes were quantified in terms of hazards ratio (HR) along with 95% confidence interval (CI) and pooled across studies using a DerSimonian-Laird random-effects model. Heterogeneity was assessed using the Cochran's Q and the I2 statistic. All hypothesis testing was two-sided with an alpha of 0.05.

RESULTS

Of 3791 studies screened, we identified 20 studies involving 3468 patients with a mean age of 60 years; 44% were female and the most common malignancy was diffuse large B-cell lymphoma (42%). Most studies measured muscle mass using single slice computed tomography imaging at the L3 level. The presence of LSMM was associated with worse OS (pooled HR = 1.81, 95% CI = 1.48-2.22, P < 0.001) with moderate heterogeneity (Cochran's Q, I2 = 60.4%), PFS (pooled HR = 1.61, 95% CI = 1.28-2.02, P < 0.001) with moderate heterogeneity (Cochran's Q, I2 = 66.0%). Similarly, LSMM was associated with worse NRM (HR = 1.72, 95% CI = 1.34-2.22, P < 0.001) with little evidence of heterogeneity (Cochran's Q, I2 = 0.0%).

CONCLUSIONS

LSMM is associated with worse survival outcomes among adults with haematologic malignancies. Further research into understanding the underlying mechanism of this association and mitigating the negative effects of LSMM among adults with haematologic malignancies is needed.

Differentiation of survival outcomes by anatomic involvement and histology with the revised 2023 International Federation of Gynecology and Obstetrics staging system for endometrial cancer

OBJECTIVES

The International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer underwent revision in 2023, incorporating histology, lymphovascular space invasion, and molecular classification. Herein, we compare overall survival (OS) outcomes by anatomic and histologic involvement for patients staged by the 2009 system versus 2023 system.

METHODS

The National Cancer Database (NCDB) was queried for patients with newly-diagnosed uterine adenocarcinoma from 2004 to 2015, with follow-up data extending through 2020. Stage was determined by both the 2009 and 2023 FIGO staging systems. Kaplan-Meier estimators and Cox proportional hazards models were used for survival analysis.

RESULTS

A total of 134,677 patients were analyzed. Per 2023 classification, patients with stage I disease decreased from 96,161 to 70,101 (-27.1%, p < 0.01), while stage II disease increased from 9295 to 36,294 (+390.5%, p < 0.01). Greatest OS change was observed for 2023 stage IA3 patients (low-risk, synchronous endometrial and ovarian tumors with a clonal relationship), whose 10-year OS was 73.4%, compared to 52.6% for 2009 stage IIIA disease. Ten-year OS for 2023 stage IIIB2 (pelvic peritoneal involvement), previously 2009 stage IVB, was 49.4%, compared to 18.7% for 2009 stage IVB patients. Akaike information criterion, Bayesian information criterion, and Harrel's concordance index were used to evaluate OS prognostication of each staging system across all stages, with likelihood ratio favoring the 2023 system (p = 0.020).

CONCLUSIONS

With FIGO's 2023 endometrial cancer anatomic and histologic staging system, stage migration is greatest in early-stage disease. New staging groups may offer more precise prognostication. These changes may affect future management.

Guidelines for Neuroprognostication in Critically Ill Adults with Moderate-Severe Traumatic Brain Injury

BACKGROUND

Moderate-severe traumatic brain injury (msTBI) carries high morbidity and mortality worldwide. Accurate neuroprognostication is essential in guiding clinical decisions, including patient triage and transition to comfort measures. Here we provide recommendations regarding the reliability of major clinical predictors and prediction models commonly used in msTBI neuroprognostication, guiding clinicians in counseling surrogate decision-makers.

METHODS

Using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology, we conducted a systematic narrative review of the most clinically relevant predictors and prediction models cited in the literature. The review involved framing specific population/intervention/comparator/outcome/timing/setting (PICOTS) questions and employing stringent full-text screening criteria to examine the literature, focusing on four GRADE criteria: quality of evidence, desirability of outcomes, values and preferences, and resource use. Moreover, good practice recommendations addressing the key principles of neuroprognostication were drafted.

RESULTS

After screening 8125 articles, 41 met our eligibility criteria. Ten clinical variables and nine grading scales were selected. Many articles varied in defining "poor" functional outcomes. For consistency, we treated "poor" as "unfavorable". Although many clinical variables are associated with poor outcome in msTBI, only the presence of bilateral pupillary nonreactivity on admission, conditional on accurate assessment without confounding from medications or injuries, was deemed moderately reliable for counseling surrogates regarding 6-month functional outcomes or in-hospital mortality. In terms of prediction models, the Corticosteroid Randomization After Significant Head Injury (CRASH)-basic, CRASH-CT (CRASH-basic extended by computed tomography features), International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT)-core, IMPACT-extended, and IMPACT-lab models were recommended as moderately reliable in predicting 14-day to 6-month mortality and functional outcomes at 6 months and beyond. When using "moderately reliable" predictors or prediction models, the clinician must acknowledge "substantial" uncertainty in the prognosis.

CONCLUSIONS

These guidelines provide recommendations to clinicians on the formal reliability of individual predictors and prediction models of poor outcome when counseling surrogates of patients with msTBI and suggest broad principles of neuroprognostication.

SOHO State of the Art Updates and Next Questions: An Update on Higher Risk Myelodysplastic Syndromes

Higher-risk myelodysplastic syndromes (HR-MDS) are clonal myeloid neoplasms that cause life-limiting complications from severe cytopenias and leukemic transformation. Efforts to better classify, prognosticate, and assess therapeutic responses in HR-MDS have resulted in publication of new clinical tools in the last several years. Given limited current treatment options and suboptimal outcomes, HR-MDS stands to benefit from the study of investigational agents.Higher-risk myelodysplastic syndromes (HR-MDS) are a heterogenous group of clonal myeloid-lineage malignancies often characterized by high-risk genetic lesions, increased blood transfusion needs, constitutional symptoms, elevated risk of progression to acute myeloid leukemia (AML), and therapeutic need for allogeneic bone marrow transplantation. Use of blast percentage and other morphologic features to define myelodysplastic neoplasm subtypes is rapidly shifting to incorporate genetics, resulting in a subset of former HR-MDS patients now being considered as AML in presence of leukemia-defining genetic alterations. A proliferation of prognostic tools has further focused use of genetic features to drive decision making in clinical management. Recently, criteria to assess response of HR-MDS to therapy were revised to incorporate more clinically meaningful endpoints and better match AML response criteria. Basic science investigations have resulted in improved understanding of the relationship between MDS genetic lesions, bone marrow stromal changes, germline predispositions, and disease phenotype. However, therapeutic advances have been more limited. There has been import of the IDH1 inhibitor ivosidenib, initially approved for AML; the Bcl-2 inhibitor venetoclax and liposomal daunorubicin/cytarabine (CPX-351) are under active investigation as well. Unfortunately, effective treatment of TP53-mutated disease remains elusive, though preliminary evidence suggests improved outcomes with oral decitabine/cedazuridine over parenteral hypomethylating agent monotherapy. Investigational agents with novel mechanisms of action may help expand the repertoire of treatment options for HR-MDS and trials continue to offer a hopeful therapeutic avenue for suitable patients.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND

Liver transplantation (LT) is a life-saving intervention for patients with end-stage liver disease. However, the equitable allocation of scarce donor organs remains a formidable challenge. Prognostic tools are pivotal in identifying the most suitable transplant candidates. Traditionally, scoring systems like the model for end-stage liver disease have been instrumental in this process. Nevertheless, the landscape of prognostication is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence models.

AIM

To assess the utility of ML models in prognostication for LT, comparing their per formance and reliability to established traditional scoring systems.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we conducted a thorough and standardized literature search using the PubMed/MEDLINE database. Our search imposed no restrictions on publication year, age, or gender. Exclusion criteria encompassed non-English stu dies, review articles, case reports, conference papers, studies with missing data, or those exhibiting evident methodological flaws.

RESULTS

Our search yielded a total of 64 articles, with 23 meeting the inclusion criteria. Among the selected studies, 60.8% originated from the United States and China combined. Only one pediatric study met the criteria. Notably, 91% of the studies were published within the past five years. ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values (ranging from 0.6 to 1) across all studies, surpassing the performance of traditional scoring systems. Random forest exhibited superior predictive capa bilities for 90-d mortality following LT, sepsis, and acute kidney injury (AKI). In contrast, gradient boosting excelled in predicting the risk of graft-versus-host disease, pneumonia, and AKI.

CONCLUSION

This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT, marking a significant evolution in the field of prognostication.

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma

PURPOSE

Machine learning (ML) models have been used to predict cancer survival in several sarcoma subtypes. However, none have investigated extremity leiomyosarcoma (LMS). ML is a powerful tool that has the potential to better prognosticate extremity LMS.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologic extremity LMS (n = 634). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of extremity LMS patients (n = 46).

RESULTS

All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.75-0.76 at the 5-year time point. The Random Forest (RF) model was the best performing model and used for external validation. This model also performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.90 and 0.87, respectively. The RF model was well calibrated on external validation. This model has been made publicly available at https://rachar.shinyapps.io/lms_app/ CONCLUSIONS: ML models had excellent performance for survival prediction of extremity LMS. Future studies incorporating a larger institutional cohort may be needed to further validate the ML model for LMS prognostication.

The capacity of neurological pupil index to predict the absence of somatosensory evoked potentials after cardiac arrest - An observational study

Background

In neurologic prognostication of comatose survivors from cardiac arrest, two independent predictors of poor outcome are the loss of the Pupillary light reflex (PLR) and the loss of the N20 response from Somatosensory Evoked potentials (SSEP). The PLR can be quantitatively assessed by pupillometry. Both tests depend on the midbrain, in which a dysfunction reflects a severe hypoxic injury. We reasoned that a certain level of defective PLR would be predictive of a bilaterally absent SSEP N20 response.

Method

Neurological Pupil index (NPi) from the pupillometry and the SSEP N20 response were registered >48 h after cardiac arrest in comatose survivors. Clinical data were retrospectively analyzed. A receiver operating characteristic curve was used to evaluate the capacity of NPi to predict bilaterally absent SSEP N20 response. An NPi threshold value resulting in <5% false positive rate (FPR) for bilaterally absent N20 response was identified.

Results

From February 2020 to August 2022, we included 54 patients out of which 49 had conclusive pupillometry and SSEP examinations. The NPi threshold value with FPR < 5% was 3.4, yielding 36% sensitivity (95% CI 18-55) and significantly discriminated between respective groups with preserved and bilaterally absent N20 response to SSEP (p-value <0.01).

Conclusion

In this limited cohort, NPi < 3.4 in patients remaining comatose >48 hours after cardiac arrest predicted bilateral loss of the SSEP N20 response with a FPR < 5%. If validated in a larger cohort, an NPi threshold may be clinically applied in settings where SSEP is unavailable.

Predictive value and interrater reliability of mental status and mobility assessment in the emergency department

AIM

To investigate the predictive value of both mental status, assessed with the AVPUC (Alert, responds to Voice, responds to Pain, Unresponsive, and new Confusion) scale, and mobility assessments, and their interrater reliability (IRR) between triage clinicians and a research team.

METHOD

Prospective study of consecutive patients who presented to an ED. Mental status and mobility were assessed by triage clinicians and by a dedicated research team.

RESULTS

4,191 patients were included. After adjustment for age and sex, patients with altered mental status have an odds ratio of 6.55 [4.09-10.24] to be admitted in the ICU and an odds ratio of 21.16 [12.06-37.01] to die within 30 days; patients with impaired mobility have an odds ratio of 7.08 [4.60-11.12] to be admitted in the ICU and an odds ratio of 12.87 [5.93-32.30] to die within 30 days. The kappa coefficient between triage clinicians and the research team for mental status assessment was 0.75, and 0.80 for mobility.

CONCLUSION

Assessment of mental status by the AVPUC scale, and mobility by a simple dichotomous scale are suitable for ED triage. Both altered mental status and impaired mobility are associated with adverse outcomes. Mental status and mobility assessment have good interrater reliability.

Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma

PURPOSE

Machine learning (ML) algorithms to predict cancer survival have recently been reported for a number of sarcoma subtypes, but none have investigated undifferentiated pleomorphic sarcoma (UPS). ML is a powerful tool that has the potential to better prognosticate UPS.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologically confirmed undifferentiated pleomorphic sarcoma (UPS) (n = 665). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of UPS patients (n = 151).

RESULTS

All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.67-0.69 at the 5-year time point. The Multi-Layer Perceptron Neural Network (MLP) model was the best performing model and used for external validation. Similarly, the MLP model performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.85 and 0.81, respectively. The MLP model was well calibrated on external validation. The MLP model has been made publicly available at https://rachar.shinyapps.io/ups_app/ .

CONCLUSION

Machine learning models perform well for survival prediction in UPS, though this sarcoma subtype may be more difficult to prognosticate than other subtypes. Future studies are needed to further validate the machine learning approach for UPS prognostication.

Doctors and nurses subjective predictions of 6-month outcome compared to actual 6-month outcome for adult patients with spontaneous intracerebral haemorrhage (ICH) in neurocritical care: An observational study

Background

Acute spontaneous intracerebral haemorrhage is a devastating form of stroke. Prognostication after ICH may be influenced by clinicians' subjective opinions.

Purpose

To evaluate subjective predictions of 6-month outcome by clinicians' for ICH patients in a neurocritical care using the modified Rankin Scale (mRS) and compare these to actual 6-month outcome.

Method

We included clinicians' predictions of 6-month outcome in the first 48 h for 52 adults with ICH and compared to actual 6-month outcome using descriptive statistics and multilevel binomial logistic regression.

Results

35/52 patients (66%) had a poor 6-month outcome (mRS 4-6); 19/52 (36%) had died. 324 predictions were included. For good (mRS 0-3) versus poor (mRS 4-6), outcome, accuracy of predictions was 68% and exact agreement 29%. mRS 6 and mRS 4 received the most correct predictions. Comparing job roles, predictions of death were underestimated, by doctors (12%) and nurses (13%) compared with actual mortality (36%). Predictions of vital status showed no significant difference between doctors and nurses: OR = 1.24 {CI; 0.50-3.05}; (p = 0.64) or good versus poor outcome: OR = 1.65 {CI; 0.98-2.79}; (p = 0.06). When predicted and actual 6-month outcome were compared, job role did not significantly relate to correct predictions of good versus poor outcome: OR = 1.13 {CI;0.67-1.90}; (p = 0.65) or for vital status: OR = 1.11 {CI; 0.47-2.61}; p = 0.81).

Conclusions

Early prognostication is challenging. Doctors and nurses were most likely to correctly predict poor outcome but tended to err on the side of optimism for mortality, suggesting an absence of clinical nihilism in relation to ICH.

Introduction to the novel model of preoperative Multi - Domain Risk Stratification (pMDRS)

Preoperative risk stratification is an important step in surgical procedures. The current scoring systems do not predict accurate overall surgical outcomes in complex comorbid patients. The novel model of preoperative multi-domain risk stratification is described in this article, which categorises patients in to three risk groups, aiming to modify the risk for optimal surgical outcomes.

One-Year Clinical Outcomes and Prognostic Factors Following Revascularization in Patients With Acute Limb Ischemia - Results From the RESCUE ALI Study

BACKGROUND

Acute limb ischemia (ALI) is a limb- and life-threatening condition and urgent treatment including revascularization should be offered to patients unless the limb is irreversibly ischemic. The aim of this study was to investigate 1-year clinical outcomes and prognostic factors following revascularization in patients with ALI.Methods and Results: A retrospective, multicenter, nonrandomized study examined 185 consecutive patients with ALI treated by surgical revascularization (SR), endovascular revascularization (ER), or hybrid revascularization (HR) in 6 Japanese medical centers from January 2015 to August 2021. The 1-year amputation-free survival (AFS) rate was estimated to be 69.2% (95% confidence interval [CI], 62.8-76.2%). There were no significant differences among SR, ER, and HR regarding both technical success and perioperative complications. Multivariate analysis revealed that Rutherford category IIb and III ischemia (hazard ratio [HR]: 1.86; 95% CI: 1.06-3.25), supra- to infrapopliteal lesion (HR: 2.06; 95% CI: 1.08-3.95), and technical failure (HR: 2.58; 95% CI: 1.49-4.46) were independent risk factors for 1-year AFS.

CONCLUSIONS

Rutherford category IIb and III ischemia, supra- to infrapopliteal lesions, and technical failures were identified as independent risk factors for 1-year AFS. Furthermore, patients with multiple risk factors had a lower AFS rate.

Prognostication and Trajectories of Recovery in Disorders of Consciousness

Historically, there has been a pessimistic view regarding outcomes for patients with disorders of consciousness (DoC). There is a paucity of clinical diagnostic tools and prognostic protocols. Guidelines for the care of patients with DoC require behavioral observation, time, resources, and knowledge of the population. Many nonclinical factors such as patient wishes, family perception, and personal finances can indirectly influence long-term outcomes. Prognostic expectations need to be considered but we health-care professional cannot fully appreciate the decisions and influence of those decisions on the person served or on the care providers involved.

Cognitive Motor Dissociation: Gap Analysis and Future Directions

BACKGROUND

Patients with disorders of consciousness who are behaviorally unresponsive may demonstrate volitional brain responses to motor imagery or motor commands detectable on functional magnetic resonance imaging or electroencephalography. This state of cognitive motor dissociation (CMD) may have prognostic significance.

METHODS

The Neurocritical Care Society's Curing Coma Campaign identified an international group of experts who convened in a series of monthly online meetings between September 2021 and April 2023 to examine the science of CMD and identify key knowledge gaps and unmet needs.

RESULTS

The group identified major knowledge gaps in CMD research: (1) lack of information about patient experiences and caregiver accounts of CMD, (2) limited epidemiological data on CMD, (3) uncertainty about underlying mechanisms of CMD, (4) methodological variability that limits testing of CMD as a biomarker for prognostication and treatment trials, (5) educational gaps for health care personnel about the incidence and potential prognostic relevance of CMD, and (6) challenges related to identification of patients with CMD who may be able to communicate using brain-computer interfaces.

CONCLUSIONS

To improve the management of patients with disorders of consciousness, research efforts should address these mechanistic, epidemiological, bioengineering, and educational gaps to enable large-scale implementation of CMD assessment in clinical practice.

Improving prognostication in patients with hepatocellular carcinoma undergoing loco-regional therapy using pre- and post-locoregional therapy scores

BACKGROUND

Many scoring systems have been proposed for predicting survival in patients with hepatocellular carcinoma (HCC) undergoing locoregional therapy (LRT). We aimed to study the role of the NIACE score, hepatoma arterial embolization prognostic score (HAP), and ABCR score in predicting transplant-free survival (TFS) in these patients.

METHODS

In this retrospective multicenter study of a United States Veteran cohort who underwent LRT, NIACE, HAP, and ABCR scores were calculated, and their predictive accuracy for TFS within different modified BCLC (mod-BCLC) stages was analyzed.

RESULTS

180 subjects underwent LRT between January-2012 and March-2019 were followed till January-2022, mean age 65.6 ± 6.3 years, model for end-stage liver disease -sodium (MELD-Na) score (at first LRT) 14.1 ± 6.7. A total of 43.9%, 35%, and 21.1% of patients had mod-BCLC A, B, and C stage disease, respectively. A total of 76.7% underwent transarterial embolization (TAE), 6.1% underwent ablation, and 17.2% underwent transarterial radioembolization (TARE) as the first intervention and were followed for a median of 576.5 patient-years. The NIACE score, HAP score, and ABCR scores differentiated patients within mod-BCLC stages A and B into groups with significant differences in TFS. In the stratified analysis of those undergoing only TAE, all three scores identified subgroups with significantly different TFS.

CONCLUSION

In patients with HCC undergoing LRT, the mod-BCLC stages have subgroups with variable overall TFS. The NIACE score, HAP score, and ABCR score identified differential prognoses is within mod-BCLC stages and characterized subgroups with different TFS following LRT (TAE). Integration of these scoring systems into treatment decisions would help to improve prognostication within respective mod-BCLC groups, which may help with more customized treatment allocation.

Mastering the brain in critical conditions: an update

Acute brain injuries, such as traumatic brain injury and ischemic and hemorragic stroke, are a leading cause of death and disability worldwide. While characterized by clearly distict primary events-vascular damage in strokes and biomechanical damage in traumatic brain injuries-they share common secondary injury mechanisms influencing long-term outcomes. Growing evidence suggests that a more personalized approach to optimize energy substrate delivery to the injured brain and prognosticate towards families could be beneficial. In this context, continuous invasive and/or non-invasive neuromonitoring, together with clinical evaluation and neuroimaging to support strategies that optimize cerebral blood flow and metabolic delivery, as well as approaches to neuroprognostication are gaining interest. Recently, the European Society of Intensive Care Medicine organized a 2-day course focused on a practical case-based clinical approach of acute brain-injured patients in different scenarios and on future perspectives to advance the management of this population. The aim of this manuscript is to update clinicians dealing with acute brain injured patients in the intensive care unit, describing current knowledge and clinical practice based on the insights presented during this course.

Neuroimaging correlation with EEG in status epilepticus

PURPOSE

This study was to correlate EEG patterns with peri‑ictal perfusion CT abnormality (PCA) or peri‑ictal MR abnormality (PMA) in patients with status epilepticus (SE).

METHODS

This is a retrospective study done with SE patients from January 2016 to December 2021. We defined the PCA as single or multi-territorial cortical and/or thalamic hyper-perfusion. The PMA was defined as increased signal intensity in multiple brain regions including the cortex and subcortical regions. EEG patterns were categorized into electrographic seizure (ESz)/electroclinical seizure (ECSz), ictal-interictal continuum (IIC), and lateralized periodic discharges (LPDs) per the American Clinical Neurophysiology Society's guideline. We analyzed the association between the patterns of EEG and the presence of PCA or PMA.

RESULTS

Among 73 patients, 26 % (19/73) showed PCA and 25 % (18/73) demonstrated PMA. The patterns of EEG were as follows; ESz/ECSz in 25 % (18/73), IIC in 34 % (25/73), and LPD in 12 % (9/73). There was a significant correlation between the patterns of EEG and the presence of PMA, but not PCA. 48 % (12/25) had both PMA and PCA whereas 52 % (13/25) showed either PMA (6/25) or PCA (7/25).

CONCLUSION

Although PCA did not reveal an electro-radiographical correlation, PMA was strongly linked to ESz, ECSz, IIC, and LPD.

How different are accuracies of clinicians' prediction of survival by assessment methods?

BACKGROUND

No standardized method has been established for evaluating the accuracy of a clinicians' prediction of survival (CPS). Till now, no study has compared the accuracy of CPS according to the evaluation methods using the same dataset. We aimed to examine the accuracy of CPS by different statistical approaches in patients with far-advanced cancer.

METHODS

The current study was a secondary analysis of an international multicenter prospective cohort study. Newly admitted patients with advanced cancer were enrolled in palliative care units (PCUs) in Japan, Korea, and Taiwan. We obtained the temporal CPS at enrollment. The patients were classified into groups of days (≤7 days) and weeks (≤30 days) based on CPS and actual survival (AS). We evaluated the accuracy of CPS by the distribution, area under the receiver operating characteristics curve (AUROCs), and an estimate ±33% of AS.

RESULTS

A total of 2,571 patients were assessed and admitted in 37 PCUs between January 2017 and September 2018. As for the "days" category, the distribution of AS is larger than that of CPS, however, the results are reversed in the "weeks" category. The AUROCs showed over 80% discrimination for both the "days" and "weeks" categories. Accurate CPS within ±33% of AS was approximately 30% in both "days" and "weeks" categories.

CONCLUSIONS

We showed a discrepancy of approximately 30-80% in the accuracy of CPS among three different analysis methods: distribution, AUROC, and AS comparison. Considering the low accuracy of AS comparisons, clinicians should provide a wide range of survival time. CPS was able to effectively discriminate and may be useful for risk stratification.

Prognostic role of inferior vena collapsibility index in congenital heart disease: A validation study

BACKGROUND

A recent study showed that inferior vena cava collapsibility index (IVCCI) <60% had better prognostic performance as compared to the American Society of Echocardiogram (ASE) criteria for estimating right atrial pressure (RAP). However, this study was based on a selected cohort of adults with congenital heart disease (CHD) that underwent right heart catheterization and limiting the generalizability of the results. The purpose of this study was, therefore, to validate the prognostic performance of IVCCI in a more representative sample of adults with CHD, which would in turn, improve generalizability of the results.

METHODS

Retrospective cohort study of adults with CHD that underwent echocardiogram at Mayo Clinic (2003-2021). Elevated RAP was defined as RAP >10 mmHg, and was estimated using IVCCI <60% or the ASE criteria (maximum IVC diameter < 2.1 cm and IVCCI <50%). Cardiovascular event was defined as heart failure hospitalization, heart transplant or cardiovascular death.

RESULTS

Of the 4029 patients, 754 (19%) and 601 (15%) had elevated RAP (RAP >10 mmHg) based on IVCCI <60%, and the ASE criteria, respectively. Of the 4029 patients, 374 (9%) had cardiovascular events during 7.6 (4.4-10.5) years of follow-up. IVCCI <60% was independently associated with cardiovascular events (adjusted HR 2.08, 95% CI 1.75-2.42; C-statistic 0.708, 95%CI 0.688-0.728), and provided improved prognostic performance as compared to the ASE criteria (C-statistic difference 0.036, 95%CI 0.017-0.055, P = 0.008).

CONCLUSIONS

IVCCI had superior prognostic performance as compared to the ASE criteria.

Predicting Prostate Surgery Outcomes from Standard Clinical Assessments of Lower Urinary Tract Symptoms To Derive Prognostic Symptom and Flowmetry Criteria

BACKGROUND

Assessment of male lower urinary tract symptoms (LUTS) needs to identify predictors of symptom outcomes when interventional treatment is planned.

OBJECTIVE

To develop a novel prediction model for prostate surgery outcomes and validate it using a separate patient cohort and derive thresholds for key clinical parameters.

DESIGN, SETTING, AND PARTICIPANTS

From the UPSTREAM trial of 820 men seeking treatment for LUTS, analysis of bladder diary (BD), International Prostate Symptom Score (IPSS), IPSS-quality of life, and uroflowmetry data was performed for 176 participants who underwent prostate surgery and provided complete data. For external validation, data from a retrospective database of surgery outcomes in a Japanese urology department (n = 227) were used.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Symptom improvement was defined as a reduction in total IPSS of ≥3 points. Multiple logistic regression, classification tree analysis, and random forest models were generated, including versions with and without BD data.

RESULTS AND LIMITATIONS

Multiple logistic regression without BD data identified age (p = 0.029), total IPSS (p = 0.0016), and maximum flow rate (Qmax; p = 0.066) as predictors of outcomes, with area under the receiver operating characteristic curve (AUC) of 77.1%. Classification tree analysis without BD data gave thresholds of IPSS <16 and Qmax ≥13 ml/s (AUC 75.0%). The random forest model, which included all clinical parameters except BD data, had an AUC of 94.7%. Internal validation using the bootstrap method showed reasonable AUCs (69.6-85.8%). Analyses using BD data marginally improved the model fits. External validation gave comparable AUCs for logistic regression, classification tree analysis, and random forest models (all without BD; 70.9%, 67.3%, and 68.5%, respectively). Limitations include the significant number of men with incomplete baseline data and limited assessments in the external validation cohort.

CONCLUSIONS

Outcomes of prostate surgery can be predicted preoperatively using age, total IPSS, and uroflowmetry data, with prognostic thresholds of 16 for IPSS and 13 ml/s for Qmax.

PATIENT SUMMARY

This study identified key preoperative factors that can predict outcomes of prostate surgery for bothersome urinary symptoms, including which patients are at risk of a poor outcome.

Measurement of thalamus and cortical damages in hypoxic ischemic encephalopathy

Background

The thalamic gray-white matter ratios (GWRs) on CT and quantitative suppression ratios (SRs) of background activities on EEG may reflect damages in the thalamus and cerebral hemispheres in patients with hypoxic-ischemic encephalopathy (HIE).

Methods

The inclusion criteria were (1) cardiac arrest patients over the age of 20 years from March 2010 to March 2020, and (2) patients who had both EEG and brain CT within 7 days after cardiac arrest. The thalamic GWRs were semi-quantitatively measured by using the region of interest (ROI). SRs of background were analyzed with the installed software (Persyst® v13) in EEG machine.

Results

175 patients were included among 686 patients with HIE and the thalamic GWRs of 168 patients were successfully measured. 155 patients (89 %) showed poor outcomes. The poor outcome group revealed not only higher SRs, but also lower thalamic GWRs. The thalamic GWRs showed a negative correlation to the SRs (ρ (rho) = -0.36, p < 0.0001 for right side, ρ (rho) = -0.31, p < 0.0001 for left side). The good outcome group showed neither beyond the cut-off values of thalamic GWRs nor SRs [40 % (59/148) VS 0 % (0/20) in right side, p = 0.0005 %, and 28 % (42/148) VS 0 % (0/20) in left side, p = 0.0061].

Conclusion

The thalamic GWRs and SRs may reflect the damage in the thalamus and cerebral hemispheres in patients with HIE. Insults in the thalamocortical circuit (TCC) or the thalamus might be responsible for the poor outcome.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.

Burkitt Lymphoma Presenting as Ileocolic Intussusception in an Adult

Adult intussusception is rare, highly associated with a malignant lead point, and often requires emergent surgical management. We report the case of a 44-year-old male who presented with generalized abdominal pain and was found to have early ileocolic intussusception secondary to a large ileocecal mass. Biopsies of the mass and an enlarged cardiophrenic lymph node, as well as pleural fluid cytology were all consistent with Burkitt lymphoma (BL). Curiously, the patient's abdominal exam was reassuring, and the intussusception and malignant bowel obstruction resolved over 36 hours with conservative management alone. With a Burkitt lymphoma international prognostic index (BL-IPI) score of 2, the patient proceeded to treatment with combination chemoimmunotherapy and attained a complete response after four cycles. There was no bowel perforation or recurrent intussusception throughout treatment. Thus, this report marks the first reported case of adult BL-associated intussusception to resolve with non-invasive management and establishes a precedent for conservative management in select patients.

Associations of Awake Prone Positioning-Induced Changes in Physiology with Intubation: An International Prospective Observational Study in Patients with Acute Hypoxemic Respiratory Failure Related to COVID-19

INTRODUCTION

Awake prone positioning has the potential to improve oxygenation and decrease respiratory rate, potentially reducing the need for intubation in patients with acute hypoxemic respiratory failure. We investigated awake prone positioning-induced changes in oxygenation and respiratory rate, and the prognostic capacity for intubation in patients with COVID-19 pneumonia.

METHODS

International multicenter prospective observation study in critically ill adult patients with COVID-19 receiving supplemental oxygen. We collected data on oxygenation and respiratory rate at baseline, and at 1 h after being placed in prone positioning. The combined primary outcome was oxygenation and respiratory rate at 1 h. The secondary endpoint was treatment failure, defined as need for intubation within 24 h of start of awake prone positioning.

RESULTS

Between March 27th and November 2020, 101 patients were enrolled of which 99 were fully analyzable. Awake prone positioning lasted mean of 3 [2-4] h. In 77 patients (77.7%), awake prone positioning improved oxygenation, and in 37 patients (54.4%) it decreased respiratory rate. Twenty-nine patients (29.3%) were intubated within 24 h. An increase in SpO2/FiO2 of < 10 (OR 5.1, 95% CI 1.4-18.5, P = 0.01), a failure to increase PaO2/FiO2 to > 116 mmHg (OR 3.6, 95% CI 1.2-10.8, P = 0.02), and a decrease in respiratory rate of < 2 breaths/min (OR 3.6, 95% CI 1.3-9.5, P = 0.01) were independent variables associated with need for intubation. The AUC-ROC curve for intubation using a multivariable model was 0.73 (95% CI 0.62-0.84).

CONCLUSIONS

Awake prone positioning improves oxygenation in the majority of patients, and decreases respiratory rate in more than half of patients with acute hypoxemic respiratory failure caused by COVID-19. One in three patients need intubation within 24 h. Awake prone position-induced changes in oxygenation and respiratory rate have prognostic capacity for intubation within 24 h.

Consultation on UTUC II Stockholm 2022: diagnostics, prognostication, and follow-up-where are we today?

PURPOSE

To summarise the current knowledge regarding diagnostics, prognostication and follow-up in upper tract urothelial carcinoma (UTUC).

METHODS

A scoping review combined with expert opinion was applied to provide an overview of the current research field. Based on the published literature and the experts' own experience and opinions, consensus was reached through presentations and discussions at the meeting Consultation on UTUC II in Stockholm 2022.

RESULTS

The strongest prognostic factors in UTUC are tumour grade and stage. They are correlated, and grade is used for indirect staging. The diagnostic examinations should include multiphase computed tomography urography (CTU) with corticomedullary phase, and urethrocystoscopy with cytology. If there is no clear diagnosis for clinical decision-making, ureterorenoscopy (URS) with focal cytology and biopsies should be performed. Both WHO classification systems (1973/1999 and 2004/2016) should be used. Novel biomarker tests are not yet widespread nor recommended for the detection of UTUC. Long-term, regular follow-up, including URS in patients who have had organ-sparing treatment, is important to check for tumour recurrences, intravesical recurrences, metastases and progression of the tumour.

CONCLUSION

Proper diagnostics with correct grading of UTUC are necessary for appropriate treatment decisions. The diagnostics should include CTU with corticomedullary phase, urine or bladder cytology, URS with focal barbotage cytology, and biopsies when needed for proper diagnosis and risk stratification. Regular, long-term follow-ups are fundamental, due to the high rate of recurrence and risk of progression.

Quantitative pupillometry for neuroprognostication in comatose post-cardiac arrest patients: A protocol for a predefined sub-study of the Blood pressure and Oxygenations Targets after Out-of-Hospital Cardiac Arrest (BOX)-trial

Background

Resuscitation guidelines propose a multimodal prognostication strategy algorithm at ≥72 hours after the return of spontaneous circulation to evaluate neurological outcome for unconscious cardiac arrest survivors. Even though guidelines suggest quantitative pupillometry for assessing pupillary light reflex, threshold values are not yet validated.This study aims to validate pre-specified thresholds of quantitative pupillometry by quantitatively assessing the percentage reduction of pupillary size (qPLR) <4% and Neurological Pupil index (NPi) ≤2 and in predicting unfavorable neurological outcome. Both as an isolated predictor and combined with guideline-suggested neuron-specific enolase (NSE) threshold >60 μg L-1 in the current prognostication strategy algorithm.

Methods

We conduct this pre-planned diagnostic sub-study in the randomized, controlled, multicenter clinical trial "Blood Pressure and Oxygenation Targets after Out-of-Hospital Cardiac Arrest-trial". Blinded to treating physicians and outcome assessors, measurements of qPLR and NPi are obtained from cardiac arrest survivors at time points (±6 hours) of admission, after 24, 48, and 72 hours, or until the time of awakening or death.

Discussion

This study will be the largest prospective study investigating the predictive performance of automated quantitative pupillometry in unconscious patients resuscitated from cardiac arrest. We will test specific threshold values of NPi ≤2 and qPLR <4% to predict unfavorable outcome following cardiac arrest. The validation of pupillometry alone and combined with NSE with the criteria of the current prognostication strategy algorithm will hopefully increase the level of evidence and support clinical neuroprognostication with automated quantitative pupillometry in unconscious post-cardiac arrest patients.

Trial registration

Registered March 30, 2017, at ClinicalTrials.gov (Identifier: NCT03141099).

Inflammatory biomarkers to predict the prognosis of acute bacterial and viral infections

Mortality in acute infections is mostly associated with sepsis, defined as 'life-threatening organ dysfunction caused by a dysregulated host response to infection'. It remains challenging to identify the patients with increased mortality risk due to the high heterogeneity in the dysregulated host immune response and disease progression. Biomarkers reflecting different pathways involved in the inflammatory response might improve prediction of mortality risk (prognostic enrichment) among patients with acute infections by reducing heterogeneity of the host response, as well as suggest novel strategies for patient stratification and treatment (predictive enrichment) through precision medicine approaches. The predictive value of inflammatory biomarkers has been extensively investigated in bacterial infections and the recent COVID-19 pandemic caused an increased interest in inflammatory biomarkers in this viral infection. However, limited research investigated whether the prognostic potential of these biomarkers differs between bacterial and viral infections. In this narrative review, we provide an overview of the value of various inflammatory biomarkers for the prediction of mortality in bacterial and viral infections.

Postictal Encephalopathy After Status Epilepticus: Outcome and Risk Factors

BACKGROUND

Postictal encephalopathy is well known after status epilepticus (SE), but its prognostic impact and triggers are unknown. Here, we aimed to establish risk factors for the development of postictal encephalopathy and to study its impact on survival after discharge.

METHODS

This retrospective cohort study comprised adult patients diagnosed with first nonanoxic SE at Odense University Hospital between January 2008 and December 2017. Patients with ongoing SE at discharge or unknown treatment success were excluded. Postictal symptoms of encephalopathy were estimated retrospectively using the West Haven Criteria (WHC). WHC grade was determined for postictal day 1 to 14 or until the patient died or was discharged from the hospital. Cumulative postictal WHC during 14 days after SE-cessation was used to quantify postictal encephalopathy. Clinical characteristics, patient demographics, electroencephalographic and imaging features, and details on intensive care treatment were assessed from medical records.

RESULTS

Of all eligible patients (n = 232), 198 (85.3%) had at least WHC grade 2 postictal encephalopathy that lasted for > 14 days in 24.5% of the surviving patients. WHC grade at discharge was strongly associated with poor long-term survival (p < 0.001). Postictal encephalopathy was not associated with nonconvulsive SE, postictal changes on magnetic resonance imaging, or distinct ictal patterns on electroencephalography. Although duration of SE and treatment in the intensive care unit showed an association with cumulative postictal WHC grade, they were not independently associated with the degree of encephalopathy when controlling for confounders. In a linear regression model, etiology, duration of sedation, age, and premorbid modified Rankin Scale were significant and consistent predictors for higher cumulative postictal WHC grade. Exploratory analyses showed an association of a cumulative midazolam dosage (mg/kg/h) with higher cumulative postictal WHC grade.

DISCUSSION

In this cohort, postictal encephalopathy after SE was common and associated with poor long-term survival. Seizure characteristics were not independently associated with postictal encephalopathy; the underlying etiology, long (high-dose midazolam) sedation, high age, and poor premorbid condition were the major risk factors for its development.

Efficacy of induction chemotherapy in lymph node-positive stage III nasopharyngeal carcinoma and identification of beneficiaries based on clinical features: A propensity score matching analysis

PURPOSE

To investigate the role of induction chemotherapy (IC) in lymph node-positive (LN-positive) stage III nasopharyngeal carcinoma (NPC) receiving concurrent chemoradiotherapy (CCRT).

METHODS

In total, 627 patients with newly diagnosed LN-positive stage III NPC receiving CCRT or IC plus CCRT were included. The primary endpoint was progression-free survival (PFS). Propensity-score matching (PSM) was conducted to balance the intergroup covariates. Kaplan-Meier method with log-rank test was employed to compare survival curves. Subgroup analyses were conducted based on baseline characteristics.

RESULTS

After 1:1 PSM, 414 patients were identified (207 patients per group). Compared with CCRT, IC plus CCRT provided better survival (5-year PFS 88.4% vs. 78.6%, P = 0.01; overall survival [OS] 94.8% vs. 85.3%, P = 0.003; and distant metastasis-free survival [DMFS] 93.1% vs. 85.6%, P = 0.03). The IC beneficial effects on PFS were mainly present in patients with grade 2-3 ENE, elevated serum lactate dehydrogenase (LDH > 170U/L), and N2 disease. Patients with grade 2 CNN had comparable PFS benefits to those with grade 0-1 CNN. For patients with grade 0-1 ENE combined with LDH ≤ 170U/L, survival between the two groups was similar with 5-year PFS 93.6% vs. 90.4% (P = 0.50), OS 94.2% vs. 93.0% (P = 0.72), and DMFS 98.6% vs. 97.7% (P = 0.98).

CONCLUSION

Adding IC before CCRT improved survival in LN-positive stage III NPC patients. Additional IC did not provide better survival for patients with grade 0-1 ENE combined with LDH ≤ 170U/L and could be avoided in this population. CNN may not be a good risk factor for tailoring a personalized treatment plan.

Correlation and Prognostic Significance of Oxygenation Indices in Invasively Ventilated Adults (OXIVA-CARDS) with COVID-19-associated ARDS: A Retrospective Study

Background

Oxygenation index [OI = (MAP × FiO2 × 100)/PaO2] assesses the severity of hypoxic respiratory failure. Oxygen saturation index [OSI = (MAP × FiO2 × 100)/SpO2] is a noninvasive method to assesses the severity of hypoxic respiratory failure. Conventionally used PaO2/FiO2 (P/F) ratio to measure the severity of ARDS requires arterial blood gas (ABG) sampling. It tenders limited prognostic information mandating the need for better markers. Oxygenation index (needs arterial sampling) and OSI (a noninvasive method) are substitutes to provide mortality information in ARDS patients. We evaluated the correlation between P/F, OI, and OSI in invasively ventilated COVID-19 ARDS patients (C-ARDS) and looked at its relationship with mortality.

Patients and methods

A retrospective study of invasively ventilated C-ARDS >18 years of age managed in COVID ICU. Ventilator settings (FiO2, mean airway pressure), pulse oximetry (SpO2), and ABG values (PaO2) were simultaneously noted at the time of sample collection. Patient outcomes (alive and deceased) were documented. Differences in parameters between survivors and nonsurvivors were assessed using independent sample t-test. Receiver operating characteristic (ROC) analysis with Youden's index was used to identify cutoff values to determine survival.

Results

A total of 1557 measurements for 203 patients were collected over the maximum duration of 21 days after ventilation. About 147 (72.4%) were males and 56 (27.6%) were females. On day one of ventilation, 161 (79.3%) had P/F ratio <200, 28 (13.8%) had P/F ratio between 200 and 300, and 14 (6.9%) had P/F ratio >300. There was a linear relationship between P/F ratio and OSI (r = -0.671), P/F and OI (r = -0.753), and OSI and OI (r = 0.893) (p < 0.001). After natural log transform, the correlation between these factors became stronger [P/F ratio and OSI (r = -0.797), PF and OI (r = -0.949), and OSI and OI (r = 0.902) (p < 0.001)]. About 74 (36.5%) patients survived. Survivors had significantly higher P/F ratio as compared with nonsurvivors (p < 0.05). Oxygen saturation index and OI were significantly lower in survivors as compared with nonsurvivors. Based on day-1 reading, a higher OSI (AUC = 0.719, 95% CI = 0.648-0.790) and OI (AUC = 0.752. 95% CI = 0.684-0.819) significantly can predict mortality. On the other hand, a higher P/F ratio can predict survival (AUC = 0.734, 95% CI = 0.664-0.805). P/F ratio of 160 on day 1 can predict survival. Oxygen saturation index values above 10.4% and OI above 13.5% were the cutoff derived for day 1 values to predict mortality.

Conclusion

Noninvasive OSI can be used to assess the severity of hypoxic respiratory failure in C-ARDS without arterial access in resource-limited settings. Oxygen saturation index can noninvasively provide prognostic information in invasively ventilated C-ARDS patients.

How to cite this article

Vadi S, Suthar D, Sanwalka N. Correlation and Prognostic Significance of Oxygenation Indices in Invasively Ventilated Adults (OXIVA-CARDS) with COVID-19-associated ARDS: A Retrospective Study. Indian J Crit Care Med 2023;27(11):801-805.

Prognostic utility of the pericoronary fat attenuation index in patients with takotsubo cardiomyopathy

BACKGROUND

The etiology of takotsubo cardiomyopathy (TCM) remains poorly understood and no optimal management strategy has been established. Identification of features associated with poor outcomes may improve the prognosis of patients with TCM. We aimed to identify the predictors of poor prognosis in patients with TCM using coronary computed tomography angiography (CCTA).

METHODS

We enrolled consecutive patients with TCM who underwent CCTA during the acute disease phase. The pericoronary fat attenuation index (FAI) of adipose tissue was obtained from CCTA images. Major adverse cardiac and cerebrovascular events (MACCE) were defined as all-cause death, non-fatal myocardial infarction, stroke, rehospitalization due to congestive heart failure, and TCM recurrence. The relationships between patient characteristics and CCTA findings were compared between patients with and without MACCE.

RESULTS

A total of 52 patients were included (10 men [19.2%]; mean age, 71 years). After a median follow-up of 23 months, MACCE had developed in 10 patients (19.2%). There were significant differences in clinical characteristics [including the three-vessel mean FAI (FAI-mean)] between patients with and without MACCE. Univariate Cox regression analyses showed that FAI-mean ​≥ ​-68.94 Hounsfield units (cut-off value derived from receiver operating characteristic curve analysis) (hazard ratio [HR], 13.52; 95% confidence interval [CI], 1.705-107.2; p ​= ​0.014) and NT-proBNP (HR, 1.000; 95% CI, 1.000-1.000; p ​= ​0.022) were significant predictors of MACCE. FAI-mean ​≥ ​-68.94 HU was significantly associated with MACCE (chi-squared statistic ​= ​10.3, p ​= ​0.001).

CONCLUSION

In patients with TCM, a higher FAI-mean was significantly associated with poorer outcomes independent of the conventional risk factors.

Assessing Physiologic Reserve and Frailty in the Older Emergency Department Patient: Should the Paradigm Change?

Older patients are more vulnerable to acute illness or injury because of reduced physiologic reserve associated with aging. Therefore, their assessment in the emergency department (ED) should include not only vital signs and their baseline values but also changes that reflect physiologic reserve, such as mobility, mental status, and frailty. Combining aggregated vitals sign scores and frailty might improve risk stratification in the ED. Implementing these changes in ED assessment may require the introduction of senior-friendly processes to ensure ED treatment is appropriate to the older patients' immediate discomfort, personal goals, and likely prognosis.

Changes in Sarcopenia Status Predict Survival Among Patients with Resectable Esophageal Cancer

BACKGROUND

Sarcopenia is a predictor of survival in patients with esophageal cancer. The objective of this research was to obtain insight into how changes in sarcopenia influence survival in resectable esophageal cancer.

PATIENTS AND METHODS

A retrospective cohort of patients with esophageal cancer undergoing tri-modality therapy was selected. Body composition parameters from the staging, post-neoadjuvant, and 1-year surveillance computed tomography (CT) scans were calculated. Overall survival (OS) and disease-free survival (DFS) were evaluated using the Kaplan-Meier method and log-rank test, as well as multivariable Cox-proportional hazards models.

RESULTS

Of 141 patients, 118 had images at all three timepoints. The median DFS and OS were 33.2 [95% confidence interval (CI) 19.1-73.7] and 34.5 (95% CI 23.1-57.6) months, respectively. Sarcopenia classified by the staging CT was present in 20 (17.0%) patients. This changed to 45 (38.1%) patients by the post-neoadjuvant scan, and 44 (37.3%) by the surveillance scan. In multivariable analysis, sarcopenia at the post-neoadjuvant scan was significantly associated with OS [hazards ratio (HR) 2.65, 95% CI 1.59-4.40; p < 0.001] and DFS (HR 1.80, 95% CI 1.03-3.13; p = 0.038). The net change in skeletal muscle index was associated with OS (HR 0.93, 95% CI 0.90-0.97; p < 0.001) and DFS (HR 0.94, 95% CI 0.91-0.98; p = 0.001).

CONCLUSIONS

Patients who develop sarcopenia as a consequence of skeletal muscle wasting during neoadjuvant therapy are at risk for worse DFS and OS. Patients who have a net loss of muscle over time may be at high risk for early disease recurrence.

Confounders for prognostic accuracy of neuron-specific enolase after cardiac arrest: A retrospective cohort study

AIM

To evaluate neuron-specific enolase (NSE) thresholds for prediction of neurological outcome after cardiac arrest and to analyze the influence of hemolysis and confounders.

METHODS

Retrospective analysis from a cardiac arrest registry. Determination of NSE serum concentration and hemolysis-index (h-index) 48-96 hours after cardiac arrest. Evaluation of neurological outcome using the Cerebral Performance Category score (CPC) at hospital discharge. Separate analyses considering CPC 1-3 and CPC 1-2 as good neurological outcome. Analysis of specificity and sensitivity for poor and good neurological outcome prediction with and without exclusion of hemolytic samples (h-index larger than 50).

RESULTS

Among 356 survivors three days after cardiac arrest, hemolysis was detected in 28 samples (7.9%). At a threshold of 60 µg/L, NSE predicted poor neurological outcome (CPC 4-5) in all samples with a specificity of 92% (86-95%) and sensitivity of 73% (66-79%). In non-hemolytic samples, specificity was 94% (89-97%) and sensitivity 70% (62-76%). At a threshold of 100 µg/L, specificity was 98% (95-100%, all samples) and 99% (95-100%, non-hemolytic samples), and sensitivity 58% (51-65%) and 55% (47-63%), respectively. Possible confounders for elevated NSE in patients with good neurological outcome were ECMO, malignancies, blood transfusions and acute brain diseases. Nine patients with NSE below 17 µg/L had CPC 5, all had plausible death causes other than hypoxic-ischemic encephalopathy.

CONCLUSIONS

NSE concentrations higher than 100 µg/L predicted poor neurological outcome with high specificity. An NSE less than 17 µg/L indicated absence of severe hypoxic-ischemic encephalopathy. Hemolysis and other confounders need to be considered.

INSTITUTIONAL PROTOCOL NUMBER

The local ethics committee (board name: Ethikkommission der Charité) approved this study by the number: EA2/066/23, approval date: 28th June 2023, study title "'ROSC' - Resuscitation Outcome Study."

suPARnostic: an advanced predictive tool for detecting recurrence in renal cell carcinoma

BACKGROUND

Plasma soluble urokinase-type Plasminogen Activator Receptor (suPAR) predicts disease aggressiveness in renal cell carcinoma (ccRCC), but its prognostic accuracy has not been investigated. To investigate the prognostic accuracy of preoperative plasma suPAR in patients who received curative treatment for initially localized ccRCC.

METHODS

We retrospectively analyzed plasma samples stored in the Danish National Biobank between 2010 and 2015 from 235 patients with ccRCC at any stage. Relationships with outcome analyzed using univariate and multiple logistic Cox regression analysis.

RESULTS

There were 235 patients with ccRCC. The median follow-up period was 7.7 years. In univariate analysis suPAR ≥ 6 ng/mL was significantly associated with overall survival (OS) and recurrence-free survival (RFS). Patients with elevated suPAR were more likely to recur, with a Hazard Ratio (HR) of 2.3 for RFS. In multiple logistic regression, suPAR ≥ 6 ng/mL remained a negative predictor of OS and RFS. Limitations include retrospective study design, wide confidence intervals, and tumor subtype heterogeneity bias.

CONCLUSIONS

ccRCC patients with high plasma suPAR concentrations are at an elevated risk of disease recurrence and see lower OS. suPAR is a promising surveillance tool to more precisely follow up with ccRCC patients and detect future recurrences. In this study, we showed that new type of liquid marker in blood plasma, called suPAR, is associated to a higher risk of kidney cancer recurrence when elevated above 6ng/mL. We also showed suPAR to independently be able to predict patients overall and recurrence free survival in patient with any stage of kidney cancer.

Conditional survival following radical cystectomy for urothelial carcinoma of the bladder

INTRODUCTION

Traditional surveillance protocols do not adequately account for the decreasing risk of mortality over time in aggressive malignancies, such as bladder cancer. Rather, the risk of death depends on both the baseline risk of mortality and the time survived since treatment. We therefore evaluated the conditional survival of patients diagnosed with urothelial carcinoma of the bladder (UCB) following radical cystectomy (RC).

PATIENTS AND METHODS

We identified patients aged 18 to 75 with Charlson 0-1 and pTany pN0-3 cM0 UCB diagnosed from 2006 to 2015 in the National Cancer Database and treated with RC. The 2- and 5-year conditional overall survival (COS)-i.e., the probability of surviving an additional 2- or 5-years given a specified time survived since treatment-was estimated using the Kaplan-Meier method. Multivariable Cox regression models with landmark time analysis were used to evaluate the associations of baseline characteristics with OS over time.

RESULTS

A total of 15,594 patients were included in the study. Median follow-up was 27.8 months. The 2- and 5-year COS for the overall cohort increased through 36 months follow-up and then plateaued. When stratified by pT and pN stage, the COS gain increased with higher pT and pN stage, demonstrating the greatest increase over time for patients with pTany N1-3 disease (5-year COS of 23% at baseline, 58% at 36-months, and 71% at 60-months). In multivariable Cox regression modeling, pT and pN stage were significantly associated with higher all-cause mortality at baseline (HR 3.27 for pT4, HR 2.57 for pT3 vs. ≤pT2; HR 2.26 for pN2-3, HR 1.77 for pN1 vs. pN0), but these associations were attenuated in magnitude with increasing landmark times of 36- and 60-months (HR 1.63 for pT4, HR 1.35 for pT3 vs. ≤pT2; HR 1.34 for pN2-3, HR 1.27 for pN1 vs. pN0). Our study is limited by the retrospective design and the lack of cancer-specific survival data.

CONCLUSIONS

Risk of death after RC varies with time elapsed since treatment and disease stage. Accordingly, stage-specific COS may be used to improve prognostication and surveillance protocols.

Factors Associated with Unfavorable Clinical Presentations in Patients with Ruptured BrainArteriovenous Malformations

BACKGROUND

Rupture of brain arteriovenous malformations (bAVMs) carries potentially devastating consequences. For patients presenting with ruptured bAVMs, several clinical grading systems have been shown to predict long-term patient morbidity and may be taken into consideration when making clinical decisions. Unfortunately, use of these scoring systems is typically limited to their prognostic value and offer little to patients in therapeutic benefit. Tools are needed not only to predict prognosis for patients experiencing ruptured bAVMs but to gain insight into what characteristics predispose patients to poor long-term outcomes before they rupture. Our objective was to find clinical, morphologic, and demographic variables that correlate with unfavorable clinical grades on presentation in patients with ruptured bAVMs.

METHODS

We retrospectively reviewed a cohort of patients with ruptured bAVMs. Linear regression models were used to test whether Glasgow Coma Scale (GCS) and Hunt-Hess scores on presentation(outcomes) were associated with patient and arteriovenous malformation (AVM) characteristics (predictors) individually.

RESULTS

GCS and Hunt-Hess were assessed following bAVM rupture for 121 brain cases. The median age at rupture was 28.5 years, and 62 (51%) were female. Smoking history was associated with worse GCS; current and past smokers had GCS scores 1.33 points lower on average than nonsmokers (95% confidence interval [CI] -2.59 to -0.07, P = 0.039) and had worse Hunt-Hess scores (0.42, 95% CI 0.07-0.77, P = 0.019). Associated aneurysms were associated with worse GCS (-1.60, 95% CI -3.16 to -0.05, P = 0.043) and trended towards worse Hunt-Hess scores (0.42 points, 95% CI -0.01 to 0.86, P = 0.057).

CONCLUSIONS

Patient smoking status and presence of an AVM associated aneurysm were shown to have modest correlations with unfavorable clinical grades (Hunt-Hess, GCS) on presentation, with unfavorable clinical grades being associated with long-term patient prognosis following bAVM rupture. Further investigation using AVM-specific grading scales and external data are needed to determine the utility of these and other variables in clinical practice for patients with bAVM.

Long-term clinical outcomes in critically ill patients with sepsis and pre-existing low muscle mass: a retrospective cohort study

PURPOSE

Critically ill patients with sepsis account for significant disease morbidity and healthcare costs. Low muscle mass has been proposed as an independent risk factor for poor short-term outcomes, although its effect on long-term outcomes remains unclear.

METHODS

Retrospective cohort analysis of patients treated at a quaternary care medical center over 6 years (09/2014 - 12/2020). Critically ill patients meeting Sepsis-3 criteria were included, with low muscle mass defined by [Formula: see text] 5th percentile skeletal muscle index, measured at the L3 lumbar level (L3SMI) on Computed-Tomography (CT) scan ([Formula: see text] 41.6 cm2/m2 for males and [Formula: see text] 32.0 cm2/m2 for females). L3SMI was calculated by normalizing the CT-measured skeletal muscle area to the square of the patient's height (in meters). Measurements were taken from abdominal/pelvic CT scan obtained within 7 days of sepsis onset. The prevalence of low muscle mass and its association with clinical outcomes, including in-hospital and one-year mortality, and post-hospitalization discharge disposition in survivors, was analyzed. Unfavorable post-hospitalization disposition was defined as discharge to a location other than the patient's home.

RESULTS

Low muscle mass was present in 34 (23%) of 150 patients, with mean skeletal muscle indices of 28.0 ± 2.9 cm2/m2 and 36.8 ± 3.3 cm2/m2 in females and males, respectively. While low muscle mass was not a significant risk factor for in-hospital mortality (hazard ratio 1.33; 95% CI 0.64 - 2.76; p = 0.437), it significantly increased one-year mortality after adjusting for age and illness severity using Cox multivariate regression (hazard ratio 1.9; 95% CI 1.1 - 3.2; p = 0.014). Unfavorable post-hospitalization discharge disposition was not associated with low muscle mass, after adjusting for age and illness severity in a single, multivariate model.

CONCLUSION

Low muscle mass independently predicts one-year mortality but is not associated with in-hospital mortality or unfavorable hospital discharge disposition in critically ill patients with sepsis.

Heart rate and heart rate variability as a prognosticating feature for functional outcome after cardiac arrest: A scoping review

Background

Despite significant progress in cardiopulmonary resuscitation and post-cardiac arrest care, favorable outcome in out-of hospital sudden cardiac arrest patients remains low. One of the main reasons for mortality in these patients is withdrawal of life-sustaining treatment. There is a need for precise and equitable prognostication tools to support families in avoiding premature or inappropriate WLST. Heart rate (HR) and heart rate variability (HRV) have been noted for their association with outcome, and are positioned to be a useful modality for prognostication.

Objectives

The aim of this scoping review is to rigorously explore which electrocardiography features have been shown to predict functional outcome in post-cardiac arrest patients.

Methods

The search was performed in Pubmed, EMBASE, and SCOPUS for studies published from January 1, 2011, to September 29, 2022, including papers in English or Korean.

Results

Seven studies were included with a total of 1359 patients. Four studies evaluated HR, one study evaluated RR inverval, and two studies evaluated HRV. All studies were retrospective, with 3 multi-center and 4 single-center studies. All seven studies were inclusive of patients who underwent targeted temperature management (TTM) after cardiac arrest, and two studies included patients without TTM. Five studies used cerebral performance category to assess functional outcome, two studies used Glasgow outcome score, and one study used modified Rankin scale. Three studies measured outcome at hospital discharge, one study measured outcome at 14 days after return of spontaneous circulation, two studies measured outcome after 3 months, and one after 1 year. In all studies that evaluated HR, lower HR was associated with favorable functional outcome. Two studies found that higher complexity of HRV was associated with favorable functional outcome.

Conclusion

HR and HRV showed clear associations with functional outcome in patients after CA, but cinilcial utility for prognostication is uncertain.

ECMO PAL: using deep neural networks for survival prediction in venoarterial extracorporeal membrane oxygenation

PURPOSE

Venoarterial extracorporeal membrane oxygenation (VA-ECMO) is a complex and high-risk life support modality used in severe cardiorespiratory failure. ECMO survival scores are used clinically for patient prognostication and outcomes risk adjustment. This study aims to create the first artificial intelligence (AI)-driven ECMO survival score to predict in-hospital mortality based on a large international patient cohort.

METHODS

A deep neural network, ECMO Predictive Algorithm (ECMO PAL) was trained on a retrospective cohort of 18,167 patients from the international Extracorporeal Life Support Organisation (ELSO) registry (2017-2020), and performance was measured using fivefold cross-validation. External validation was performed on all adult registry patients from 2021 (N = 5015) and compared against existing prognostication scores: SAVE, Modified SAVE, and ECMO ACCEPTS for predicting in-hospital mortality.

RESULTS

Mean age was 56.8 ± 15.1 years, with 66.7% of patients being male and 50.2% having a pre-ECMO cardiac arrest. Cross-validation demonstrated an inhospital mortality sensitivity and precision of 82.1 ± 0.2% and 77.6 ± 0.2%, respectively. Validation accuracy was only 2.8% lower than training accuracy, reducing from 75.5% to 72.7% [99% confidence interval (CI) 71.1-74.3%]. ECMO PAL accuracy outperformed the ECMO ACCEPTS (54.7%), SAVE (61.1%), and Modified SAVE (62%) scores.

CONCLUSIONS

ECMO PAL is the first AI-powered ECMO survival score trained and validated on large international patient cohorts. ECMO PAL demonstrated high generalisability across ECMO regions and outperformed existing, widely used scores. Beyond ECMO, this study highlights how large international registry data can be leveraged for AI prognostication for complex critical care therapies.

Soluble TREM-1 plasma concentration predicts poor outcome in COVID-19 patients

BACKGROUND

The immuno-receptor Triggering Expressed on Myeloid cells-1 (TREM-1) is activated during bacterial infectious diseases, where it amplifies the inflammatory response. Small studies suggest that TREM-1 could be involved in viral infections, including COVID-19. We here aim to decipher whether plasma concentration of the soluble form of TREM-1 (sTREM-1) could predict the outcome of hospitalized COVID-19 patients.

METHODS

We conducted a multicentre prospective observational study in 3 university hospitals in France. Consecutive hospitalized patients with confirmed infection with SARS-CoV-2 were enrolled. Plasma concentration of sTREM-1 was measured on admission and then at days 4, 6, 8, 14, 21, and 28 in patients admitted into an ICU (ICU cohort: ICUC) or 3 times a week for patients hospitalized in a medical ward (Conventional Cohort: ConvC). Clinical and biological data were prospectively recorded and patients were followed-up for 90 days. For medical ward patients, the outcome was deemed complicated in case of requirement of increased oxygen supply > 5 L/min, transfer to an ICU, or death. For Intensive Care Unit (ICU) patients, complicated outcome was defined by death in the ICU.

RESULTS

Plasma concentration of sTREM-1 at inclusion was higher in ICU patients (n = 269) than in medical ward patients (n = 562) (224 pg/mL (IQR 144-320) vs 147 pg/mL (76-249), p < 0.0001), and higher in patients with a complicated outcome in both cohorts: 178 (94-300) vs 135 pg/mL (70-220), p < 0.0001 in the ward patients, and 342 (288-532) vs 206 pg/mL (134-291), p < 0.0001 in the ICU patients. Elevated sTREM-1 baseline concentration was an independent predictor of complicated outcomes (Hazard Ratio (HR) = 1.5 (1.1-2.1), p = 0.02 in ward patients; HR = 3.8 (1.8-8.0), p = 0.0003 in ICU patients). An sTREM-1 plasma concentration of 224 pg/mL had a sensitivity of 42%, and a specificity of 76% in the ConvC for complicated outcome. In the ICUC, a 287 pg/mL cutoff had a sensitivity of 78%, and a specificity of 74% for death. The sTREM-1 concentrations increased over time in the ConvC patients with a complicated outcome (p = 0.017), but not in the ICUC patients.

CONCLUSIONS

In COVID-19 patients, plasma concentration of sTREM-1 is an independent predictor of the outcome, although its positive and negative likelihood ratio are not good enough to guide clinical decision as a standalone marker.

Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

BACKGROUND

Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes.

METHODS

An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness.

RESULTS

The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH.

CONCLUSIONS

We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

Value of EEG in outcome prediction of hypoxic-ischemic brain injury in the ICU: A narrative review

Prognostication of comatose patients after cardiac arrest aims to identify patients with a large probability of favourable or unfavouble outcome, usually within the first week after the event. Electroencephalography (EEG) is a technique that is increasingly used for this purpose and has many advantages, such as its non-invasive nature and the possibility to monitor the evolution of brain function over time. At the same time, use of EEG in a critical care environment faces a number of challenges. This narrative review describes the current role and future applications of EEG for outcome prediction of comatose patients with postanoxic encephalopathy.

Constructing an individualized surveillance framework for nasopharyngeal carcinoma based on a dynamic risk-adapted approach

BACKGROUND AND PURPOSE

This study aims to evaluate the dynamic survival and recurrence hazard of nasopharyngeal carcinoma(NPC) patients after definitive chemoradiotherapy utilizing conditional survival(CS) analysis, and to propose a personalized surveillance strategy at different clinical stages.

MATERIALS AND METHODS

Non-metastatic NPC patients who received curative chemotherapy between June 2005 and December 2011 were included. The Kaplan-Meier method was used to calculate the CS rate.

RESULTS

A total of 1616 patients were analyzed. With the prolongation of survival time, both conditional locoregional recurrence free survival and distant metastatic free survival increased gradually. Changing pattern of annual recurrence risk over time varied among different clinical stages. The annual locoregional recurrence(LRR) risk in stage I-II was always less than 2%, while in stage III-IVa, it was greater than 2% for the first three years and decreased to below 2% only after the third year. The annual distant metastases (DM) risk was always less than 2% in stage I, but higher than 2% in stage II for the first 3 years (2.5-3.8%). For those with stage III-IVa, the annual DM risk retained at a high level(>5%), and only decreased to < 5% after the third year. Based on the dynamic changes in survival probability over time, we established a surveillance plan with different follow-up intensities and frequencies for different clinical stages.

CONCLUSION

The annual risk of LRR and DM decrease over time. Our individual surveillance model will provide critical prognostic information to optimize clinical decision-making, and promote to formulate surveillance counseling and help with resources allocation.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.