Development and validation of BLOOMY prediction scores for 14-day and 6-month mortality in hospitalised adults with bloodstream infections: a multicentre, prospective, cohort study

A comprehensive, predictive mortality score for patients with bloodstream infections (PROBAC): a prospective, multicentre cohort study

OBJECTIVES

Bloodstream infections (BSI) are an important cause of mortality, although they show heterogeneity depending on patients and aetiological factors. Comprehensive and specific mortality scores for BSI are scarce. The objective of this study was to develop a mortality predictive score in BSI based on a multicentre prospective cohort.

METHODS

A prospective cohort including consecutive adults with bacteraemia recruited between October 2016 and March 2017 in 26 Spanish hospitals was randomly divided into a derivation cohort (DC) and a validation cohort (VC). The outcome was all-cause 30-day mortality. Predictors were assessed the day of blood culture growth. A logistic regression model and score were developed in the DC for mortality predictors; the model was applied to the VC.

RESULTS

Overall, 4102 patients formed the DC and 2009 the VC. Mortality was 11.8% in the DC and 12.34% in the CV; the patients and aetiological features were similar for both cohorts. The mortality predictors selected in the final multivariate model in the DC were age, cancer, liver cirrhosis, fatal McCabe underlying condition, polymicrobial bacteraemia, high-risk aetiologies, high-risk source of infection, recent use of broad-spectrum antibiotics, stupor or coma, mean blood pressure <70 mmHg and PaO2/FiO2 ≤ 300 or equivalent. Mortality in the DC was <2% for ≤2 points, 6%-14% for 3-7 points, 26%-45% for 8-12 points and ≥60% for ≥13 points. The predictive score had areas under the receiving operating curves of 0.81 (95% CI 0.79-0.83) in the DC and 0.80 (0.78-0.83) in the VC.

CONCLUSIONS

A 30 day mortality predictive score in BSI with good discrimination ability was developed and internally validated.

Assessment of a novel BLOOMY score for predicting mortality in hospitalised adults with bloodstream infection

PURPOSE

A German multicentre study BLOOMY was the first to use machine learning approach to develop mortality prediction scores for bloodstream infection (BSI) patients, but the scores have not been assessed in other cohorts. Our aim was to assess how the BLOOMY 14-day and 6-month scores estimate mortality in our cohort of 497 cases with BSI.

METHODS

Clinical data, laboratory data, and patient outcome were gathered retrospectively from patient records. The scores were calculated as presented in the BLOOMY study with the exception in the day of the evaluation.

RESULTS

In our cohort, BLOOMY 14-day score estimated death by day 14 with an area under curve (AUC) of 0.87 (95% Confidence Interval 0.80-0.94). Using ≥ 6 points as a cutoff, sensitivity was 68.8%, specificity 88.1%, positive predictive value (PPV) 39.3%, and negative predictive value (NPV) 96.2%. These results were similar in the original BLOOMY cohort and outweighed both quick Sepsis-Related Organ Failure Assessment (AUC 0.76) and Pitt Bacteraemia Score (AUC 0.79) in our cohort. BLOOMY 6-month score to estimate 6-month mortality had an AUC of 0.79 (0.73-0.85). Using ≥ 6 points as a cutoff, sensitivity was 98.3%, specificity 10.7%, PPV 25.7%, and NPV 95.2%. AUCs of 6-month score to estimate 1-year and 5-year mortality were 0.80 (0.74-0.85) and 0.77 (0.73-0.82), respectively.

CONCLUSION

The BLOOMY 14-day and 6-month scores performed well in the estimations of mortality in our cohort and exceeded some established scores, but their adoption in clinical work remains to be seen.

Genetic correlations and causal relationships between cardio-metabolic traits and sepsis

Cardio-metabolic traits have been reported to be associated with the development of sepsis. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability, or are confounded by environmental factors. We performed three analyses to explore the relationships between cardio-metabolic traits and sepsis. Mendelian randomization (MR) study to evaluate the causal effects of multiple cardio-metabolic traits on sepsis. Global genetic correlation analysis to explore the correlations between cardio-metabolic traits and sepsis. Local genetic correlation (GC) analysis to explore shared genetic heritability between cardio-metabolic traits and sepsis. Some loci were further examined for related genes responsible for the causal relationships. Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies with sample sizes between 200,000 to 750,000. In MR, we found causality between BMI and sepsis (OR: 1.53 [1.4-1.67]; p < 0.001). Body mass index (BMI), which is confirmed by sensitivity analyses and multivariable MR adjusting for confounding factors. Global GC analysis showed a significant correlation between BMI and sepsis (rg = 0.55, p < 0.001). More cardio-metabolic traits were identified to be correlated to the sepsis onset such as CRP (rg = 0.37, p = 0.035), type 2 diabetes (rg = 0.33, p < 0.001), HDL (rg = - 0.41, p < 0.001), and coronary artery disease (rg = 0.43, p < 0.001). Local GC revealed some shared genetic loci responsible for the causality. The top locus 1126 was located at chromosome 7 and comprised genes HIBADH, JAZF1, and CREB5. The present study provides evidence for an independent causal effect of BMI on sepsis. Further detailed analysis of the shared genetic heritability between cardio-metabolic traits and sepsis provides the opportunity to improve the preventive strategies for sepsis.

A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients

The aim of the study was to build a machine learning-based predictive model to discriminate between hospitalized patients at low risk and high risk of bloodstream infection (BSI). A Data Mart including all patients hospitalized between January 2016 and December 2019 with suspected BSI was built. Multivariate logistic regression was applied to develop a clinically interpretable machine learning predictive model. The model was trained on 2016-2018 data and tested on 2019 data. A feature selection based on a univariate logistic regression first selected candidate predictors of BSI. A multivariate logistic regression with stepwise feature selection in five-fold cross-validation was applied to express the risk of BSI. A total of 5660 hospitalizations (4026 and 1634 in the training and the validation subsets, respectively) were included. Eleven predictors of BSI were identified. The performance of the model in terms of AUROC was 0.74. Based on the interquartile predicted risk score, 508 (31.1%) patients were defined as being at low risk, 776 (47.5%) at medium risk, and 350 (21.4%) at high risk of BSI. Of them, 14.2% (72/508), 30.8% (239/776), and 64% (224/350) had a BSI, respectively. The performance of the predictive model of BSI is promising. Computational infrastructure and machine learning models can help clinicians identify people at low risk for BSI, ultimately supporting an antibiotic stewardship approach.

Shift in risk factors for mortality by period of the bloodstream infection timeline

BACKGROUND

This study was designed to determine changes in risk factors on the prognosis of patients during each period of the bloodstream infection (BSI) timeline.

METHODS

Through an integrated study of multivariable regressions with machine learning techniques, the risk factors for mortality during each period of BSI were analyzed.

RESULTS

A total of 302,303 inpatients who underwent blood cultures during 2011-2021 were enrolled. More than 8 % of BSI cases progressed to subsequent BSI, and risk factors were identified as gut colonization with vancomycin-resistant enterococci (aOR 1.82; 95 % CI 1.47-2.24), intensive care unit admission (aOR 3.37; 95 % CI 3.35-4.28), and current cancer chemotherapy (aOR 1.54; 95 % CI 1.36-1.74). The mean SOFA score of the deceased patients during the first 7 days was 10.6 (SD 4.3), which was significantly higher than those on days 8-30 (7.0 ± 4.2) and after Day 30 (4.0 ± 3.5). BSIs caused by Acinetobacter baumannii and Candida albicans were more likely to result in deaths of patients for all time periods (all, P < 0.001). BSIs caused by Enterococcus faecalis and Enterococcus faecium were associated with a poor outcome in the period after Day 30 (both, P < 0.001). Nonsusceptible phenotypes to β-lactam/β-lactamase inhibitors of Escherichia coli and Klebsiella pneumoniae influenced the prognoses of patients with BSI in terms of high mortality rates during both days 8-30 and after Day 30.

CONCLUSION

Influence of microbiological factors on mortality, including BSI-causative microorganisms and their major antimicrobial resistance, was emphasized in both periods of days 8-30 and after Day 30.

An integrative model with HLA-DR, CD64, and PD-1 for the diagnostic and prognostic evaluation of sepsis

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and progressive immunosuppression with high mortality. HLA-DR, CD64, and PD-1 were assumed to be useful biomarkers for sepsis prediction. However, the ability of a combination of these biomarkers has not been clarified.

METHODS

An observational case-control study was conducted that included 30 sepsis patients, 30 critically ill patients without sepsis admitted to the intensive care unit (ICU), and 32 healthy individuals. The levels of HLA-DR, CD64, and PD-1 expression in peripheral blood immune cells and subsets was assayed on Days 1, 3, and 5, and the clinical information of patients was collected. We compared these biomarkers between groups and evaluated the predictive validity of single and combined biomarkers on sepsis mortality.

RESULTS

The results indicate that PD-1 expression on CD4- CD8- T (PD-1+ CD4- CD8- T) (19.19% ± 10.78% vs. 9.88% ± 1.79%, p = .004) cells and neutrophil CD64 index (nCD64 index) (9.15 ± 5.46 vs. 5.33 ± 2.34, p = .001) of sepsis patients were significantly increased, and HLA-DR expression on monocytes (mHLA-DR+ ) was significantly reduced (13.26% ± 8.06% vs. 30.17% ± 21.42%, p = 2.54 × 10-4 ) compared with nonsepsis critically ill patients on the first day. Importantly, the expression of PD-1+ CD4- CD8- T (OR = 0.622, 95% CI = 0.423-0.916, p = .016) and mHLA-DR+ (OR = 1.146, 95% CI = 1.014-1.295, p = .029) were significantly associated with sepsis mortality. For sepsis diagnosis, the mHLA-DR+ , PD-1+ CD4- CD8- T, and nCD64 index showed the moderate individual performance, and combinations of the three biomarkers achieved greater diagnostic value (AUC = 0.899, 95% CI = 0.792-0.962). When adding PCT into the combined model, the AUC increased to 0.936 (95% CI = 0.840-0.983). For sepsis mortality, combinations of PD-1+ CD4- CD8- T and mHLA-DR+ , have a good ability to predict the prognosis of sepsis patients, with an AUC = 0.921 (95% CI = 0.762-0.987).

CONCLUSION

These findings indicate that the combinations of HLA-DR, CD64, and PD-1 outperformed each of the single indicator in diagnosis and predicting prognosis of sepsis.

Priorities and Progress in Diagnostic Research by the Antibacterial Resistance Leadership Group

The advancement of infectious disease diagnostics, along with studies devoted to infections caused by gram-negative and gram-positive bacteria, is a top scientific priority of the Antibacterial Resistance Leadership Group (ARLG). Diagnostic tests for infectious diseases are rapidly evolving and improving. However, the availability of rapid tests designed to determine antibacterial resistance or susceptibility directly in clinical specimens remains limited, especially for gram-negative organisms. Additionally, the clinical impact of many new tests, including an understanding of how best to use them to inform optimal antibiotic prescribing, remains to be defined. This review summarizes the recent work of the ARLG toward addressing these unmet needs in the diagnostics field and describes future directions for clinical research aimed at curbing the threat of antibiotic-resistant bacterial infections.

Predictive models for short-term mortality and length of hospital stay among adults with community-onset bacteraemia before and during the COVID-19 pandemic: application of early data dynamics

BACKGROUND

The development of scoring systems to predict the short-term mortality and the length of hospital stay (LOS) in patients with bacteraemia is essential to improve the quality of care and reduce the occupancy variance in the hospital bed.

METHODS

Adults hospitalised with community-onset bacteraemia in the coronavirus disease 2019 (COVID-19) and pre-COVID-19 eras were captured as the validation and derivation cohorts in the multicentre study, respectively. Model I incorporated all variables available on day 0, Model II incorporated all variables available on day 3, and Models III, IV, and V incorporated the variables that changed from day 0 to day 3. This study adopted the statistical and machine learning (ML) methods to jointly determine the prediction performance of these models in two study cohorts.

RESULTS

A total of 3,639 (81.4%) and 834 (18.6%) patients were included in the derivation and validation cohorts, respectively. Model IV achieved the best performance in predicting 30-day mortality in both cohorts. The most frequently identified variables incorporated into Model IV were deteriorated consciousness from day 0 to day 3 and deteriorated respiration from day 0 to day 3. Model V achieved the best performance in predicting LOS in both cohorts. The most frequently identified variables in Model V were deteriorated consciousness from day 0 to day 3, a body temperature ≤ 36.0 °C or ≥ 39.0 °C on day 3, and a diagnosis of complicated bacteraemia.

CONCLUSIONS

For hospitalised adults with community-onset bacteraemia, clinical variables that dynamically changed from day 0 to day 3 were crucial in predicting the short-term mortality and LOS.

Increasing numbers and complexity of Staphylococcus aureus bloodstream infection-14 years of prospective evaluation at a German tertiary care centre with multi-centre validation of findings

OBJECTIVES

Staphylococcus aureus bloodstream infection (SAB) is a common and severe infection. This study aims to describe temporal trends in numbers, epidemiological characteristics, clinical manifestations, and outcomes of SAB.

METHODS

We performed a post-hoc analysis of three prospective SAB cohorts at the University Medical Centre Freiburg between 2006 and 2019. We validated our findings in a large German multi-centre cohort of five tertiary care centres (R-Net consortium, 2017-2019). Time-dependent trends were estimated using Poisson or beta regression models.

RESULTS

We included 1797 patients in the mono-centric and 2336 patients in the multi-centric analysis. Overall, we observed an increasing number of SAB cases over 14 years (6.4%/year and 1000 patient days, 95% CI: 5.1% to 7.7%), paralleled by an increase in the proportion of community-acquired SAB (4.9%/year [95% CI: 2.1% to 7.8%]) and a decrease in the rate of methicillin-resistant-SAB (-8.5%/year [95% CI: -11.2% to -5.6%]). All of these findings were confirmed in the multi-centre validation cohort (6.2% cases per 1000 patient cases/year [95% CI: -0.6% to 12.6%], community-acquired-SAB 8.7% [95% CI: -1.2% to 19.6%], methicillin-resistant S. aureus-SAB -18.6% [95% CI: -30.6 to -5.8%]). Moreover, we found an increasing proportion of patients with multiple risk factors for complicated/difficult-to-treat SAB (8.5%/year, 95% CI: 3.6% to 13.5%, p < 0.001), alongside an overall higher level of comorbidities (Charlson comorbidity score 0.23 points/year, 95% CI: 0.09 to 0.37, p 0.005). At the same time, the rate of deep-seated foci such as osteomyelitis or deep-seated abscesses significantly increased (6.7%, 95% CI: 3.9% to 9.6%, p < 0.001). A reduction of in-hospital mortality by 0.6% per year (95% CI: 0.08% to 1%) was observed in the subgroup of patients with infectious diseases consultations.

DISCUSSION

We found an increasing number of SAB combined with a significant increase in comorbidities and complicating factors in tertiary care centres. The resulting challenges in securing adequate SAB management in the face of high patient turnover will become an important task for physicians.

The modified lymphocyte C-reactive protein score is a promising indicator for predicting 3-year mortality in elderly patients with intertrochanteric fractures

BACKGROUND

Hip fractures are common in elderly patients, and almost all the patients undergo surgery. This study aimed to develop a novel modified lymphocyte C-reactive protein (CRP) score (mLCS) to simply and conveniently predict 3-year mortality in elderly patients undergoing intertrochanteric fracture surgery.

METHODS

A retrospective study was conducted on elderly patients who underwent intertrochanteric fracture surgery between January 2014 and December 2017. The mLCS was developed according to the value of CRP and lymphocyte counts. Univariate and multivariate Cox regression analyses were used to identify independent risk factors for 3-year mortality after surgery. The performances of the lymphocyte CRP score (LCS) and mLCS to predict 3-year mortality were then compared using C-statistics, decision curve analysis (DCA), net reclassification index (NRI) and integrated discrimination improvement (IDI).

RESULTS

A total of 291 patients were enrolled, of whom 52 (17.9%) died within 3 years after surgery. In the multivariate Cox regression analysis, mLCS (hazard ratio (HR), 5.415; 95% confidence interval (CI), 1.743-16.822; P = 0.003) was significantly associated with postoperative 3-year mortality. The C-statistics of LCS and mLCS for predicting 3-year mortality were 0.644 and 0.686, respectively. The NRI (mLCS vs. LCS, 0.018) and IDI (mLCS vs. LCS, 0.017) indicated that the mLCS performed better than the LCS. DCA also showed that mLCS had a higher clinical net benefit.

CONCLUSIONS

mLCS is a promising predictor that can simply and conveniently predict 3-year mortality in elderly patients undergoing intertrochanteric fracture surgery.

Infection Prevalence at a Tertiary Hospital in Hail, Saudi Arabia: A Single-Center Study to Identify Strategies to Improve Antibiotic Usage

Objective

Identifying the burden of disease and the condition of the Saudi population is in high demand from both a surveillance and analytical standpoint. The objective of this study was to determine the most prevalent infections among hospitalized patients (both community-acquired and hospital-acquired), the antibiotics prescribing pattern, and their relationship with patient characteristics like age and gender.

Methods

A retrospective study was conducted comprising 2646 patients with infectious diseases or complications admitted to a tertiary hospital in the Hail region of Saudi Arabia. A standardized form was used to collect information from patient's medical records. Demographic data such as age, gender, prescribed antibiotics, and culture-sensitivity tests were included in the study.

Results

Males represented about two-thirds (66.5%, n = 1760) of the patients. Most patients (45.9%) who suffered from infectious diseases were between the ages of 20 and 39. The most prevalent infectious ailment was respiratory tract infection (17.65%, n = 467). Furthermore, the most common multiple infectious diseases were gallbladder calculi with cholecystitis (40.3%, n = 69). Similarly, COVID-19 had the greatest impact on people over 60. Beta-lactam antibiotics were the most commonly prescribed (37.6%), followed by fluoroquinolones (26.26%) and macrolides (13.45%). But performing culture sensitivity tests were rather uncommon (3.8%, n = 101). For multiple infections, beta-lactam antibiotics (such as amoxicillin and cefuroxime) were the most commonly prescribed antibiotics (2.26%, n = 60), followed by macrolides (such as azithromycin and Clindamycin) and fluoroquinolones (eg, ciprofloxacin and levofloxacin).

Conclusion

Respiratory tract infections are the most prevalent infectious disease among hospital patients, who are primarily in their 20s. The frequency of performing culture tests is low. Therefore, it is important to promote culture sensitivity testing in order to support the prudent use of antibiotics. Guidelines for anti-microbial stewardship programs are also highly recommended.