Acute kidney injury (AKI) is a common disease, with high morbidity and mortality rates. In this study, we investigated the potential influence of sex and age on laboratory diagnostics and outcomes. It is known that serum creatinine (SCr) has limitations as a laboratory diagnostic parameter for AKI due to its dependence on muscle mass, which may lead to an incorrect or delayed diagnosis for certain patient groups, such as women and the elderly. Overall, 7592 cases with AKI, hospitalized at the University of Leipzig Medical Center (ULMC) between 1st January 2017 and 31st December 2019, were retrospectively analyzed. The diagnosis and staging of AKI were performed according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines, based on the level and dynamics of SCr. The impact of sex and age was analyzed by the recalculation of a female to male and an old to young SCr using the CKD-EPI equation. In our study cohort progressive AKI occurred in 19.2% of all cases (n = 1458). Female cases with AKI were underrepresented (40.4%), with a significantly lower first (−3.5 mL/min) and last eGFR (−2.7 mL/min) (p < 0.001). The highest incidence proportion of AKI was found in the [61–81) age group in female (49.5%) and male (52.7%) cases. Females with progressive AKI were underrepresented (p = 0.04). By defining and staging AKI on the basis of relative and absolute changes in the SCr level, it is more difficult for patients with low muscle mass and, thus, a lower baseline SCr to be diagnosed by an absolute SCr increase. AKIN1 and AKIN3 can be diagnosed by a relative or absolute change in SCr. In females, both stages were less frequently detected by an absolute criterion alone (AKIN1 ♀ 20.2%, ♂ 29.5%, p < 0.001; AKIN3 ♀ 13.4%, ♂ 15.2%, p < 0.001). A recalculated SCr for females (as males) and males (as young males) displayed the expected increase in AKI occurrence and severity with age and, in general, in females. Our study illustrates how SCr, as the sole parameter for the diagnosis and staging of AKI, bears the risk of underdiagnosis of patient groups with low muscle mass, such as women and the elderly. A sex- and age-adapted approach might offer advantages.

Background: Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective: With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods: Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results: We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions: AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.

(1) Background: Highly sensitive cardiac troponin T (hs-cTnT) plays an essential role in the diagnosis of myocardial injury. The upper reference limit of the respective assay is generally applied, irrespective of age, renal function, or sex. We aimed to identify age-adjusted and sex-adjusted upper reference limits in relation to renal function in a large population-based cohort without cardiac diseases. (2) Methods: We included 5428 subjects of the population-based LIFE-Adult cohort, free of diagnosed cardiac diseases. Sex-adjusted and age-adjusted 99th percentiles for hs-cTnT in subjects with preserved renal function were obtained. (3) Results: The hs-cTnT values were higher in men of all age groups. In both sexes, an increasing age positively correlated with higher hs-cTnT values. Hs-cTnT weakly correlated with serum creatinine. The three-dimensional analysis of age, creatinine, and hs-cTnT showed no relevant additional effect of creatinine on hs-cTnT. In men aged above 60 and women above 70, the calculated 99th percentiles clearly exceeded the commonly applied thresholds. (4) Conclusion: Age and sex have a major impact on the serum concentration of hs-cTnT, while renal function does not. We propose to consider age-adjusted and sex-adjusted reference values.

Background: The refeeding syndrome (RFS) is an oftentimes-unrecognized complication of reintroducing nutrition in malnourished patients that can lead to fatal cardiovascular failure. We hypothesized that a clinical decision support system (CDSS) can improve RFS recognition and management. Methods: We developed an algorithm from current diagnostic criteria for RFS detection, tested the algorithm on a retrospective dataset and combined the final algorithm with therapy and referral recommendations in a knowledge-based CDSS. The CDSS integration into clinical practice was prospectively investigated for six months. Results: The utilization of the RFS-CDSS lead to RFS diagnosis in 13 out of 21 detected cases (62%). It improved patient-related care and documentation, e.g., RFS-specific coding (E87.7), increased from once coded in 30 month in the retrospective cohort to four times in six months in the prospective cohort and doubled the rate of nutrition referrals in true positive patients (retrospective referrals in true positive patients 33% vs. prospective referrals in true positive patients 71%). Conclusion: CDSS-facilitated RFS diagnosis is possible and improves RFS recognition. This effect and its impact on patient-related outcomes needs to be further investigated in a large randomized-controlled trial.

Background and Objectives: We should control free K ⁺ during massive transfusion (>80 mL/kg) of red blood cells (RBCs) in small children. To manage this, several national and international guidelines recommend using RBCs stored only up to 7 days. We tested a washing step for RBCs in saline‐adenine‐glucose‐mannitol (SAGM) with or without irradiation to reduce supernatant K ⁺ levels, improve quality and potentially extend the shelf life of stored RBCs. Materials and Methods: RBCs of 240–330 mL were prepared from whole blood donations, then split into halves and stored in SAGM solution at 4 ± 2°C for 21 days. RBCs were split and washed on Days 1 and 8, and some were gamma‐irradiated on Day 8. Glucose, lactate, lactate dehydrogenase (LDH), adenosine triphosphate (ATP), K ⁺ and haemolysis were determined over 21 days. Results: After washing on Day 1, only glucose and lactate improved, whereas after washing on Day 8, LDH and K ⁺ also improved. Irradiation resulted in accelerated K ⁺ accumulation and increased haemolysis. Mean extracellular K ⁺ concentrations were 21.2 ± 1.03 mM after irradiation on Day 8 versus 1.12 ± 0.05 mM after irradiation plus wash on Day 8, and 38.80 ± 2.13 mM on Day 10 after irradiation on Day 8 and 16.6 ± 0.05 mM on Day 10 after irradiation plus wash on Day 8. Conclusion: K ⁺ concentrations remained <25 mM within 8 days of storage. We recommend irradiation by Day 8 at the latest for neonatal transfusion. The shelf life may be extended by another 48 h if the RBCs are also washed.

Background Delay in diagnosing sepsis results in potentially preventable deaths. Mainly due to their complexity or limited applicability, machine learning (ML) models to predict sepsis have not yet become part of clinical routines. For this reason, we created a ML model that only requires complete blood count (CBC) diagnostics. Methods Non-intensive care unit (non-ICU) data from a German tertiary care centre were collected from January 2014 to December 2021. Patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells) were utilised to train a boosted random forest, which predicts sepsis with ICU admission. Two external validations were conducted using data from another German tertiary care centre and the Medical Information Mart for Intensive Care IV database (MIMIC-IV). Using the subset of laboratory orders also including procalcitonin (PCT), an analogous model was trained with PCT as an additional feature. Findings After exclusion, 1,381,358 laboratory requests (2016 from sepsis cases) were available. The derived CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857–0.887) for predicting sepsis. External validations show AUROCs of 0.805 (95% CI, 0.787–0.824) and 0.845 (95% CI, 0.837–0.852) for MIMIC-IV. The model including PCT revealed a significantly higher performance (AUROC: 0.857; 95% CI, 0.836–0.877) than PCT alone (AUROC: 0.790; 95% CI, 0.759–0.821; p<0.001). Interpretation Our results demonstrate that routine CBC results could significantly improve diagnosis of sepsis when combined with ML. The CBC model can facilitate early sepsis prediction in non-ICU patients with high robustness in external validations. Its implementation in clinical decision support systems has strong potential to provide an essential time advantage and increase patient safety. Funding The study was part of the AMPEL project (www.ampel.care), which is co-financed through public funds according to the budget decided by the Saxon State Parliament under the RL eHealthSax 2017/18 grant number 100331796.