IMPORTANCE-Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.OBSERVATIONS-Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Objective: To analyze serial biomarkers of the persistent inflammation, immunosuppression, and catabolism syndrome (PICS) to gain insight into the pathobiology of chronic critical illness (CCI) after surgical sepsis. Background: Although early deaths after surgical intensive care unit sepsis have decreased and most survivors rapidly recover (RAP), one third develop the adverse clinical trajectory of CCI. However, the underlying pathobiology of its dismal long-term outcomes remains unclear. Methods: PICS biomarkers over 14 days from 124 CCI and 225 RAP sepsis survivors were analyzed to determine associations and prediction models for (1) CCI (≥14 intensive care unit days with organ dysfunction) and (2) dismal 1-year outcomes (Zubrod 4/5 performance scores). Clinical prediction models were created using PIRO variables (predisposition, insult, response, and organ dysfunction). Biomarkers were then added to determine if they strengthened predictions. Results: CCI (vs RAP) and Zubrod 4/5 (vs Zubrod 0–3) cohorts had greater elevations in biomarkers of inflammation (interleukin [IL]-6, IL-8, interferon gamma-induced protein [IP-10], monocyte chemoattractant protein 1), immunosuppression (IL-10, soluble programmed death ligand-1), stress metabolism (C-reactive protein, glucagon-like peptide 1), and angiogenesis (angiopoietin-2, vascular endothelial growth factor, vascular endothelial growth factor receptor-1, stromal cell-derived factor) at most time-points. Clinical models predicted CCI on day 4 (area under the receiver operating characteristics curve [AUC] = 0.89) and 1 year Zubrod 4/5 on day 7 (AUC = 0.80). IL-10 and IP-10 on day 4 minimally improved prediction of CCI (AUC = 0.90). However, IL-10, IL-6, IL-8, monocyte chemoattractant protein 1, IP-10, angiopoietin-2, glucagon-like peptide 1, soluble programmed death ligand-1, and stromal cell-derived factor on day 7 considerably improved the prediction of Zubrod 4/5 status (AUC = 0.88). Conclusions: Persistent elevations of PICS biomarkers in the CCI and Zubrod 4/5 cohorts and their improved prediction of Zubrod 4/5 validate that PICS plays a role in CCI pathobiology.

Objectives: National and international guidelines recommend empiric first-line treatments of individuals infected with Helicobacter pylori without prior antimicrobial susceptibility testing. For this reason, knowledge of primary resistance to first-line antibiotics such as clarithromycin is essential. We assessed the primary resistance of H. pylori in Germany to key antibiotics by molecular genetic methods and evaluated risk factors for the development of resistance. Methods: Gastric tissue samples of 1851 yet treatment-naïve H. pylori-positive patients were examined with real-time PCR or PCR and Sanger sequencing for mutations conferring resistance to clarithromycin, levofloxacin and tetracycline. Clinical and epidemiological data were documented and univariable and multivariable logistic regression analyses were conducted. Results: Overall primary resistances were 11.3% (210/1851) to clarithromycin, and 13.4% (201/1497) to levofloxacin; resistance to tetracycline (2.5%, 38/1497) was as low as combined resistance to clarithromycin/levofloxacin (2.6%, 39/1497). Female sex and prior antimicrobial therapies owing to unrelated bacterial infections were risk factors for clarithromycin resistance (adjusted OR (aOR) 2.3, 95% CI 1.6e3.4; and 2.6, 95% CI 1.5e4.5, respectively); older age was associated with levofloxacin resistance (aOR for those 65 years compared with those 18e35 years: 6.6, 95% CI 3.1e14.2). Conclusions: Clarithromycin might still be recommended in first-line eradication therapies in yet untreated patients, but as nearly every tenth patient may carry clarithromycin-resistant H. pylori it may be advisable to rule out resistance ahead of treatment by carrying out susceptibility testing or prescribing an alternative therapy.

Objectives Sepsis has been called a “disease of the elderly,” and as in‐hospital mortality has decreased, more sepsis survivors are progressing into poorly characterized long‐term outcomes. The purpose of this study was to describe the current epidemiology of sepsis in older adults compared with middle‐aged and young adults. Design Prospective longitudinal study with young (≤45 years), middle‐aged (46‐64 years), and older (≥65 years) patient groups. Setting University tertiary medical center. Participants A total of 328 adult surgical intensive care unit (ICU) sepsis patients. Measurements Patients were characterized by (1) baseline demographics and predisposition, (2) septic event, (3) hospital outcomes and discharge disposition, (4) 12‐month mortality, and (5) Zubrod Performance Status, physical function (Short Physical Performance Battery and handgrip strength), and cognitive function (Hopkins Verbal Learning Test, Controlled Oral Word Association, and Mini‐Mental Status Examination) at 3‐, 6‐, and 12‐month follow‐up. Loss to follow‐up was due to death (in 68), consent withdrawal (in 32), and illness and scheduling difficulties: month 3 (in 51), month 6 (in 29), and month 12 (in 20). Results Compared with young and middle‐aged patients, older patients had (1) significantly more comorbidities at presentation (eg, chronic renal disease 6% vs 12% vs 21%), intra‐abdominal infections (14% vs 25% vs 37%), septic shock (12% vs 25% vs 36%), and organ dysfunctions; (2) higher 30‐day mortality (6% vs 4% vs 17%) and fewer ICU‐free days (median = 25 vs 23 vs 20); (3) more progression into chronic critical illness (22% vs 34% vs 42%) with higher poor disposition discharge to non‐home destinations (19% vs 40% vs 62%); (4) worse 12‐month mortality (11% vs 14% vs 33%); and (5) poorer Zubrod Performance Status and objectively measured physical and cognitive functions with only slight improvement over 12‐month follow‐up. Conclusion Compared with younger patients, older sepsis survivors suffer both a higher persistent disability burden and 12‐month mortality.

Background: Increased circulating myeloid-derived suppressor cells (MDSCs) are independently associated with poor long-term clinical outcomes in sepsis. Studies implicate subsets of MDSCs having unique roles in lymphocyte suppression; however, characterization of these cells after sepsis remains incomplete. We performed a pilot study to determine the transcriptomic landscape in MDSC subsets in sepsis using single-cell RNAseq (scRNA-seq). Methods: A mixture of whole blood myeloid-enriched and Ficoll-enriched PBMCs from two late septic patients on post-sepsis day 21 and two control subjects underwent Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq). Results: We successfully identified the three MDSC subset clusters-granulocytic (G-), monocytic (M-), and early (E-) MDSCs. Sepsis was associated with a greater relative expansion of G-MDSCs versus M-MDSCs at 21 days as compared to control subjects. Genomic analysis between septic patients and control subjects revealed cell-specific and common differential expression of genes in both G-MDSC and M-MDSC subsets. Many of the common genes have previously been associated with MDSC proliferation and immunosuppressive function. Interestingly, there was no differential expression of several genes demonstrated in the literature to be vital to immunosuppression in cancer-induced MDSC. Conclusion: This pilot study successfully demonstrated that MDSCs maintain a transcriptomic profile that is immunosuppressive in late sepsis. Interestingly, the landscape in chronic critical illness is partially dependent on the original septic insult. Preliminary data would also indicate immunosuppressive MDSCs from late sepsis patients appear to have a somewhat unique transcriptome from cancer and/or other inflammatory diseases.