Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score.

Rationale: Early detection of clinical deterioration using early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective internal validation, and were not tested in important patient subgroups. Objectives: To develop a gradient boosted machine model (eCARTv5) for identifying clinical deterioration and then validate externally, test prospectively, and evaluate across patient subgroups. Methods: All adult patients hospitalized on the wards in seven hospitals from 2008- 2022 were used to develop eCARTv5, with demographics, vital signs, clinician documentation, and laboratory values utilized to predict intensive care unit transfer or death in the next 24 hours. The model was externally validated retrospectively in 21 hospitals from 2009-2023 and prospectively in 10 hospitals from February to May 2023. eCARTv5 was compared to the Modified Early Warning Score (MEWS) and the National Early Warning Score (NEWS) using the area under the receiver operating characteristic curve (AUROC). Measurements and Main Results: The development cohort included 901,491 admissions, the retrospective validation cohort included 1,769,461 admissions, and the prospective validation cohort included 46,330 admissions. In retrospective validation, eCART had the highest AUROC (0.835; 95%CI 0.834, 0.835), followed by NEWS (0.766 (95%CI 0.766, 0.767)), and MEWS (0.704 (95%CI 0.703, 0.704)). eCART's performance remained high (AUROC ≥0.80) across a range of patient demographics, clinical conditions, and during prospective validation. Conclusions: We developed eCARTv5, which accurately identifies early clinical deterioration in hospitalized ward patients. Our model performed better than the NEWS and MEWS retrospectively, prospectively, and across a range of subgroups.

Pre-morbid glycemic control modifies the interaction between acute hypoglycemia and mortality.

PURPOSE: To study the impact of pre-morbid glycemic control on the association between acute hypoglycemia in intensive care unit (ICU) patients and subsequent hospital mortality in critically ill patients. METHODS: We performed a multicenter, multinational, retrospective observational study of patients with available HbA1c levels within the 3-month period preceding ICU admission. We separated patients into three cohorts according to pre-admission HbA1c levels (<6.5, 6.5-7.9, ≥8.0%, respectively). Based on published data, we defined a glucose concentration of 40-69 mg/dL (2.2-3.8 mmol/L) as moderate hypoglycemia and <40 mg/dL (<2.2 mmol/L) as severe hypoglycemia. We applied logistic regression analysis to study the impact of pre-morbid glycemic control on the relationship between acute hypoglycemia and mortality. RESULTS: A total of 3084 critically ill patients were enrolled in the study. Among these patients, with increasing HbA1c levels from <6.5, to 6.5-7.9, and to ≥8.0%, the incidence of both moderate (3.8, 11.1, and 16.4%, respectively; p < 0.001) and severe (0.9, 2.5, and 4.3%, respectively; p < 0.001) hypoglycemia progressively and significantly increased. The relationship between the occurrence of hypoglycemic episodes in the ICU and in-hospital mortality was independently and significantly affected by pre-morbid glucose control, as assessed by adjusted odds ratio (OR) and 95 % confidence interval (CI) for hospital mortality: (1) moderate hypoglycemia: in patients with <6.5, 6.5-7.9, and ≥8.0 % of HbA1c level-OR 0.54, 95% CI 0.25-1.16; OR 0.82, 95 % CI 0.33-2.05; OR 3.42, 95 % CI 1.29-9.06, respectively; (2) severe hypoglycemia: OR 1.50, 95% CI 0.42-5.33; OR 1.59, 95% CI 0.36-7.10; OR 23.46, 95% CI 5.13-107.28, respectively (interaction with pre-morbid glucose control, p = 0.009). We found that the higher the glucose level before admission to the ICU, the higher the mortality risk when patients experienced hypoglycemia. CONCLUSIONS: In critically ill patients, chronic pre-morbid hyperglycemia increases the risk of hypoglycemia and modifies the association between acute hypoglycemia and mortality.

Using procalcitonin-guided algorithms to improve antimicrobial therapy in ICU patients with respiratory infections and sepsis.

PURPOSE OF REVIEW: In patients with systemic bacterial infections hospitalized in ICUs, the inflammatory biomarker procalcitonin (PCT) has been shown to aid diagnosis, antibiotic stewardship, and risk stratification. Our aim is to summarize recent evidence about the utility of PCT in the critical care setting and discuss the potential benefits and limitations of PCT when used for clinical decision-making. RECENT FINDINGS: A growing body of evidence supports PCT use to differentiate bacterial from viral respiratory infections (including influenza), to help risk stratify patients, and to guide decisions about optimal duration of antibiotic therapy. Different PCT protocols were evaluated for these and similar purposes in randomized controlled trials in patients with varying severities of predominantly respiratory tract infection and sepsis. These trials demonstrated effectiveness of monitoring PCT to de-escalate antibiotic treatment earlier without increasing rates of relapsing infections or other adverse outcomes. Although serial PCT measurement has shown value in risk stratification of ICU patients, PCT-guided antibiotic escalation protocols have not yet shown benefit for patients. SUMMARY: Inclusion of PCT data in clinical algorithms improves individualized decision-making regarding antibiotic treatment in patients in critical care for respiratory infections or sepsis. Future research should focus on use of repeated PCT measurements to risk-stratify patients and guide treatment to improve their outcomes.

Procalcitonin decrease over 72 hours in US critical care units predicts fatal outcome in sepsis patients.

INTRODUCTION: Close monitoring and repeated risk assessment of sepsis patients in the intensive care unit (ICU) is important for decisions regarding care intensification or early discharge to the ward. We studied whether considering plasma kinetics of procalcitonin, a biomarker of systemic bacterial infection, over the first 72 critical care hours improved mortality prognostication of septic patients from two US settings. METHODS: This retrospective analysis included consecutively treated eligible adults with a diagnosis of sepsis from critical care units in two independent institutions in Clearwater, FL and Chicago, IL. Cohorts were used for derivation or validation to study the association between procalcitonin change over the first 72 critical care hours and mortality. RESULTS: ICU/in-hospital mortality rates were 29.2%/31.8% in the derivation cohort (n=154) and 17.6%/29.4% in the validation cohort (n=102). In logistic regression analysis of both cohorts, procalcitonin change was strongly associated with ICU and in-hospital mortality independent of clinical risk scores (Acute Physiology, Age and Chronic Health Evaluation IV or Simplified Acute Physiology Score II), with area under the curve (AUC) from 0.67 to 0.71. When procalcitonin decreased by at least 80%, the negative predictive value for ICU/in-hospital mortality was 90%/90% in the derivation cohort, and 91%/79% in the validation cohort. When procalcitonin showed no decrease or increased, the respective positive predictive values were 48%/48% and 36%/52%. DISCUSSION: In septic patients, procalcitonin kinetics over the first 72 critical care hours provide prognostic information beyond that available from clinical risk scores. If these observations are confirmed, procalcitonin monitoring may assist physician decision-making regarding care intensification or early transfer from the ICU to the floor.

Role of procalcitonin in managing adult patients with respiratory tract infections.

Respiratory infections remain the most common reason why patients seek medical care in ambulatory and hospital settings, and they are the most frequent precursor of sepsis. In light of the limitations of clinical signs and symptoms and traditional microbiologic diagnostics for respiratory infections, blood biomarkers that correlate with the presence and extent of bacterial infections may provide additional useful information to improve diagnostic and prognostic efforts and help with therapeutic decisions in individual patients. A growing body of evidence supports the use of procalcitonin (PCT) to differentiate bacterial from viral respiratory diagnoses, to help risk stratify patients, and to guide antibiotic therapy decisions about initial need for, and optimal duration of, therapy. Although still relatively new on the clinical frontier, a series of randomized controlled trials have evaluated PCT protocols for antibiotic-related decision making and have included patients from different clinical settings and with different severities of respiratory infection. In these trials, initial PCT levels were effective in guiding decisions about the initiation of antibiotic therapy in lower-acuity patients, and subsequent measurements were effective for guiding duration of therapy in higher-acuity patients, without apparent harmful effects. Recent European respiratory infection guidelines now also recognize this concept. As with any other laboratory test, PCT should not be used on a stand-alone basis. Rather, it must be integrated into clinical protocols, together with clinical, microbiologic data and with results from clinical risk scores. The aim of this review is to summarize recent evidence about the usefulness of PCT in patients with lower respiratory tract infections and to discuss the potential benefits and limitations of this marker when used for clinical decision making.