Maria Schmidt

Background: Type 2 diabetes (T2D) is a heterogeneous metabolic disorder composed of biologically and clinically distinct subtypes, i.e. severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD), that associate with a differential risk for complications. Despite their promise for precision medicine, these clustering frameworks have been derived almost exclusively from outpatient cohorts of individuals with recent-onset T2D. However, no study has investigated the differential outcome of T2D subphenotypes in hospitalized patients.Methods: Using 8,114 adult patients admitted to a tertiary care unit (University of Leipzig Medical Center, Leipzig, Germany) for inpatient care from 2011–2022, we performed sex-stratified k-means clustering based on simple, easy-to-measure anthropometric and non-fasting clinical parameters, i.e. age, body mass index, glycated hemoglobin A1c, and triglyceride-to-high density lipoprotein cholesterol ratio (TG/HDL-C). Standardized inpatient outcomes included organ dysfunction (e.g. kidney replacement therapy and ventilation support), intensive care unit/intermediate care (ICU/IMC) admission, and in-hospital mortality. We applied machine learning-based approaches to predict cluster allocation and developed a publicly available online-calculator.Results: Clustering of T2D patients in hospitalized individuals based on routinely available easy-to-measure inpatient clinical markers successfully reproduces the well-established subphenotypes from population-based studies. Patients from the SIRD cluster show most pronounced, adverse phenotypes, including increased inflammation and impaired renal function at baseline. The SIRD cluster was associated with highest rates of mechanical ventilation and kidney replacement therapy during inpatient care, as well as ICU/IMC admission. Furthermore, SIRD and MARD patients were associated with significantly increased in-hospital mortality after adjustment for comorbidities. A multinomial logistic regression model predicted cluster membership with 92% accuracy. Conclusion: Our findings demonstrate that T2D subphenotypes can be identified using widely available, easily accessible clinical markers at hospital admission, enabling early risk stratification for prevalent comorbidities, as well as inpatient complications, including mortality. SIRD patients show the most adverse inpatient outcome.

Timely diagnosis is crucial for sepsis treatment. Current machine learning (ML) models suffer from high complexity and limited applicability. We therefore created an ML model using only complete blood count (CBC) diagnostics. We collected non-intensive care unit (non-ICU) data from a German tertiary care centre (January 2014 to December 2021). Using patient age, sex, and CBC parameters (haemoglobin, platelets, mean corpuscular volume, white and red blood cells), we trained 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. After exclusion, 1 381 358 laboratory requests (2016 from sepsis cases) were available. The CBC model shows an area under the receiver operating characteristic (AUROC) of 0.872 (95% CI, 0.857–0.887). External validations show AUROCs of 0.805 (95% CI, 0.787–0.824) for University Medicine Greifswald and 0.845 (95% CI, 0.837–0.852) for MIMIC-IV. The model including PCT revealed a significantly higher AUROC (0.857; 95% CI, 0.836–0.877) than PCT alone (0.790; 95% CI, 0.759–0.821; P < 0.001). 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.

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.

(1) Background: Prioritization of patients for liver transplantation in Germany relies on the MELD (model for end-stage liver disease) scoring system that does not consider the patient’s sex. Many studies have shown that women are disadvantaged by the MELD score. Using a large patient cohort from a German liver transplant centre, we investigated options to reduce gender inequality in the patient prioritization for liver transplantation. (2) Methods: We calculated female-as-male MELD scores in our cohort by substituting the serum creatinine of a female patient with that of their male equivalent to test for the fairness of the scores. We investigated the effects of the female-as-male scores compared to the original MELD score of 1759 patients listed for liver transplantation. (3) Results: Serum creatinine sex correction (female-as-male) for MELD scores added up to 5.4 points in females, while the median changed by +1.6 points for females. We identified 72 females with an original MELD score < 20, for whom the adjusted female-as-male MELD score would be >20, thus giving them a better chance to receive a liver transplant. (4) Conclusions: Mathematical conversion of female to male creatinine concentrations identified disadvantages in liver transplantation prioritization for females and ascertained MELD 3.0 as having high potential to compensate for these inequalities.

Objectives: Upper reference limits of high-sensitivity cardiac troponin T (hs-cTnT) are derived from healthy, population-based cohorts, and are frequently exceeded in hospitalized patients. In this study we aim to systematically examine the differences between in-hospital patients with no diagnosed cardiac diseases and a population-based cohort. Methods: Retrospective analyses were performed in two independent cohorts. We included 5,652 participants of the prospective population-based LIFE cohort as well as 9,300 patients having been treated at our hospital between 2014 and 2021. In both cohorts, subjects with diagnosed or suspected cardiac diseases were excluded. We used Spearman’s rank correlation for correlation analyses of hs-cTnT serum concentrations and age. Sex- and age-adjusted 99th percentiles for hs-cTnT in subjects with preserved renal function were obtained in both cohorts. Results: In both cohorts, hs-cTnT serum concentrations positively correlated with age. Male sex was associated with higher hs-cTnT serum concentrations. Persons treated in hospital showed significantly higher hs-cTnT concentrations in females and males aged above 50. While in the population-based cohort only 99th percentile hs-cTnT results of females aged above 70 and males aged above 60 years exceeded the assay’s upper reference limit, the 99th percentiles of in-hospital females over 40 years and males of all age groups exceeded this threshold. Conclusions: Besides age and sex, hospitalization per se is correlated with higher serum concentrations of hs-cTnT in most age groups. Our results indicate, that unconditionally applying current hs-cTnT cut-offs to inpatients might overestimate myocardial infarction and potentially lead to overdiagnosis.

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 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; pInterpretation 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.

Resumo Objetivo: Comparar indicadores de cuidado assistencial em adultos com diagnóstico médico de diabetes mellitus (DM) no Brasil em 2013 e 2019, e analisar esses indicadores, em 2019, segundo características sociodemográficas. Métodos: Estudo transversal com dados da Pesquisa Nacional de Saúde de 2013 e 2019. Foram avaliados os indicadores de cuidado em pessoas com diagnóstico médico de DM. Resultados: A prevalência de DM aumentou de 6,2% (2013) para 7,7% (2019). Entre 2013 e 2019, ocorreu aumento no uso de medicamentos (de 80,2% para 88,8%) e de assistência médica (de 73,2% para 79,1%), houve redução no uso de medicamentos da Farmácia Popular (de 57,4% para 51,5%) e no acompanhamento com mesmo médico (de 65,2% para 59,4%). Em 2019, pessoas do sexo masculino, mais jovens, de raça/cor da pele preta e parda, menores escolaridade e renda apresentaram pior desempenho nos indicadores. Conclusão: A maioria dos indicadores permaneceu semelhante durante os últimos cinco anos, com diferenças segundo características sociodemográficas em 2019.

(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: 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.