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Abstract Background Severe acute respiratory infections (SARI) are the most common infectious causes of death. Previous work regarding mortality prediction models for SARI using machine learning (ML) algorithms that can be useful for both individual risk stratification and quality of care assessment is scarce. We aimed to develop reliable models for mortality prediction in SARI patients utilizing ML algorithms and compare its performances with a classic regression analysis approach. Methods Administrative data (dataset randomly split 75%/25% for model training/testing) from years 2016–2019 of 86 German Helios hospitals was retrospectively analyzed. Inpatient SARI cases were defined by ICD-codes J09-J22. Three ML algorithms were evaluated and its performance compared to generalized linear models (GLM) by computing receiver operating characteristic area under the curve (AUC) and area under the precision-recall curve (AUPRC). Results The dataset contained 241,988 inpatient SARI cases (75 years or older: 49%; male 56.2%). In-hospital mortality was 11.6%. AUC and AUPRC in the testing dataset were 0.83 and 0.372 for GLM, 0.831 and 0.384 for random forest (RF), 0.834 and 0.382 for single layer neural network (NNET) and 0.834 and 0.389 for extreme gradient boosting (XGBoost). Statistical comparison of ROC AUCs revealed a better performance of NNET and XGBoost as compared to GLM. Conclusion ML algorithms for predicting in-hospital mortality were trained and tested on a large real-world administrative dataset of SARI patients and showed good discriminatory performances. Broad application of our models in clinical routine practice can contribute to patients’ risk assessment and quality management.

Abstract Background The aim of our study was to assess the impact the impact of gender and age on reactogenicity to three COVID-19 vaccine products: Biontech/Pfizer (BNT162b2), Moderna (mRNA-1273) and AstraZeneca (ChAdOx). Additional analyses focused on the reduction in working capacity after vaccination and the influence of the time of day when vaccines were administered. Methods We conducted a survey on COVID-19 vaccinations and eventual reactions among 73,000 employees of 89 hospitals of the Helios Group. On May 19th, 2021 all employees received an email, inviting all employees who received at least 1 dose of a COVID-19 to participate using an attached link. Additionally, the invitation was posted in the group’s intranet page. Participation was voluntary and non-traceable. The survey was closed on June 21st, 2021. Results 8375 participants reported on 16,727 vaccinations. Reactogenicity was reported after 74.6% of COVID-19 vaccinations. After 23.0% vaccinations the capacity to work was affected. ChAdOx induced impairing reactogenicity mainly after the prime vaccination (70.5%), while mRNA-1273 led to more pronounced reactions after the second dose (71.6%). Heterologous prime-booster vaccinations with ChAdOx followed by either mRNA-1273 or BNT162b2 were associated with the highest risk for impairment (81.4%). Multivariable analyses identified the factors older age, male gender and vaccine BNT162b as independently associated with lower odds ratio for both, impairing reactogenicity and incapacity to work. In the comparison of vaccine schedules, the heterologous combination ChAdOx + BNT162b or mRNA-1273 was associated with the highest and the homologue prime-booster vaccination with BNT162b with the lowest odds ratios. The time of vaccination had no significant influence. Conclusions Around 75% of the COVID-19 vaccinations led to reactogenicity and nearly 25% of them led to one or more days of work loss. Major risk factors were female gender, younger age and the administration of a vaccine other than BNT162b2. When vaccinating a large part of a workforce against COVID-19, especially in professions with a higher proportion of young and women such as health care, employers and employees must be prepared for a noticeable amount of absenteeism. Assuming vaccine effectiveness to be equivalent across the vaccine combinations, to minimize reactogenicity, employees at risk should receive a homologous prime-booster immunisation with BNT162b2. Trial registration: The study was approved by the Ethic Committee of the Aerztekammer Berlin on May 27th, 2021 (Eth-37/21) and registered in the German Clinical Trials Register (DRKS 00025745). The study was supported by the Helios research grant HCRI-ID 2021-0272.

Abstract Aims Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in‐hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models. Methods and results Inpatient cases with primary International Statistical Classification of Diseases and Related Health Problems (ICD‐10) encoded discharge diagnosis of HF non‐electively admitted to 86 German Helios hospitals between 1 January 2016 and 31 December 2018 were identified. The dataset was randomly split 75%/25% for model development and testing. Highly unbalanced variables were removed. Four ML algorithms were applied, and all algorithms were tuned using a grid search with multiple repetitions. Model performance was evaluated by computing receiver operating characteristic areas under the curve. In total, 59 125 cases (69.8% aged 75 years or older, 51.9% female) were investigated, and in‐hospital mortality was 6.20%. Areas under the curve of all ML algorithms outperformed regression analysis in the testing dataset with values of 0.829 [95% confidence interval (CI) 0.814–0.843] for logistic regression, 0.875 (95% CI 0.863–0.886) for random forest, 0.882 (95% CI 0.871–0.893) for gradient boosting machine, 0.866 (95% CI 0.854–0.878) for single‐layer neural networks, and 0.882 (95% CI 0.872–0.893) for extreme gradient boosting. Brier scores demonstrated a good calibration especially of the latter three models. Conclusions We introduced reliable models to calculate expected in‐hospital mortality based only on administrative routine data using ML algorithms. A broad application could supplement quality measurement programs and therefore improve future HF patient care.

Abstract Aims The Covid‐19 pandemic affects care for cardiovascular conditions, but data on heart failure (HF) are scarce. This study aims to analyse HF care and in‐hospital outcomes during the pandemic in Germany. Methods and results A total of 9452 HF admissions were studied using claims data of 65 Helios hospitals; 1979 in the study period (13 March 30 April 2020) and 4691 and 2782 in two control periods (13 March to 30 April 2019 and 1 January to 12 March 2020). HF admissions declined compared with both control periods by 29–38%. Cardiac resynchronization therapy was implanted in 0.55% during the study period, 0.32% [odds ratio (OR) 1.66, 95% confidence interval (CI) 0.68–4.04, P = 0.27] in the previous year and 0.43% (OR 1.35, 95% CI 0.64–2.84, P = 0.43) in the same year control. Intensive care treatment was 6.22% during the study period, 4.49% in the previous year (OR 1.46, 95% CI 1.13–1.89, P

Abstract Background Pseudomonas aeruginosa infections are a serious threat in intensive care units (ICUs). The aim of this confirmatory, randomized, multicenter, placebo-controlled, double-blind, phase 2/3 study was to assess the efficacy, immunogenicity, and safety of IC43 recombinant Pseudomonas aeruginosa vaccine in non-surgical ICU patients. Methods Eight hundred patients aged 18 to 80 years admitted to the ICU with expected need for mechanical ventilation for ≥ 48 h were randomized 1:1 to either IC43 100 μg or saline placebo, given in two vaccinations 7 days apart. The primary efficacy endpoint was all-cause mortality in patients 28 days after the first vaccination. Immunogenicity and safety were also evaluated. Findings All-cause mortality rates at day 28 were 29.2% vs 27.7% in the IC43 and placebo groups, respectively (P = .67). Overall survival (Kaplan-Meier survival estimates, P = .46) and proportion of patients with ≥ one confirmed P. aeruginosa invasive infection or respiratory tract infection also did not differ significantly between both groups. The geometric mean fold increase in OprF/I titers was 1.5 after the first vaccination, 20 at day 28, after the second vaccination, and 2.9 at day 180. Significantly more patients in the placebo group (96.5%) had ≥ one adverse event (AE) versus the IC43 100 μg group (93.1%) (P = .04). The most frequently reported severe AEs in the IC43 and placebo groups were respiratory failure (6.9% vs 5.7%, respectively), septic shock (4.1% vs 6.5%), cardiac arrest (4.3% vs 5.7%), multiorgan failure (4.6% vs 5.5%), and sepsis (4.6% vs 4.2%). No related serious AEs were reported in the IC43 group. Interpretation The IC43 100 μg vaccine was well tolerated in this large population of medically ill, mechanically ventilated patients. The vaccine achieved high immunogenicity but provided no clinical benefit over placebo in terms of overall mortality. Trial registration https://clinicaltrials.gov (NCT01563263). Registration was sent to ClinicalTrials.gov on March 14, 2012, but posted by ClinicalTrials.gov on March 26, 2012. The first subject was included in the trial on March 22, 2012.

Background: After the first COVID-19 infection wave, a constant increase of pulmonary embolism (PE) hospitalizations not linked with active PCR-confirmed COVID-19 was observed, but potential contributors to this observation are unclear. Therefore, we analyzed associations between changes in PE hospitalizations and (1) the incidence of non-COVID-19 pneumonia, (2) the use of computed tomography pulmonary angiography (CTPA), (3) volume depletion, and (4) preceding COVID-19 infection numbers in Germany.Methods: Claims data of Helios hospitals in Germany were used, and consecutive cases with a hospital admission between May 6 and December 15, 2020 (PE surplus period), were analyzed and compared to corresponding periods covering the same weeks in 2016–2019 (control period). We analyzed the number of PE cases in the target period with multivariable Poisson general linear mixed models (GLMM) including (a) cohorts of 2020 versus 2016–2019, (b) the number of cases with pneumonia, (c) CTPA, and (d) volume depletion and adjusted for age and sex. In order to associate the daily number of PE cases in 2020 with the number of preceding SARS-CoV-2 infections in Germany, we calculated the average number of daily infections (divided by 10,000) occurring between 14 up to 90 days with increasing window sizes before PE cases and modeled the data with Poisson regression.Results: There were 2,404 PE hospitalizations between May 6 and December 15, 2020, as opposed to 2,112–2,236 (total 8,717) in the corresponding 2016–2019 control periods (crude rate ratio [CRR] 1.10, 95% CI 1.05–1.15, P < 0.01). With the use of multivariable Poisson GLMM adjusted for age, sex, and volume depletion, PE cases were significantly associated with the number of cases with pneumonia (CRR 1.09, 95% CI 1.07–1.10, P < 0.01) and with CTPA (CRR 1.10, 95% CI 1.09–1.10, P < 0.01). The increase of PE cases in 2020 compared with the control period remained significant (CRR 1.07, 95% CI 1.02–1.12, P < 0.01) when controlling for those factors. In the 2020 cohort, the number of preceding average daily COVID-19 infections was associated with increased PE case incidence in all investigated windows, i.e., including preceding infections from 14 to 90 days. The best model (log likelihood −576) was with a window size of 4 days, i.e., average COVID-19 infections 14–17 days before PE hospitalization had a risk of 1.20 (95% CI 1.12–1.29, P < 0.01).Conclusions: There is an increase in PE cases since early May 2020 compared to corresponding periods in 2016–2019. This surplus was significant even when controlling for changes in potential modulators such as demographics, volume depletion, non-COVID-19 pneumonia, CTPA use, and preceding COVID-19 infections. Future studies are needed (1) to investigate a potential causal link for increased risk of delayed PE with preceding SARS-CoV-2 infection and (2) to define optimal screening for SARS-CoV-2 in patients presenting with pneumonia and PE.

Purpose The Mid-German Sepsis Cohort (MSC) aims to investigate mid-term and long-term functional disabilities in sepsis survivors from intensive care unit (ICU) discharge until 1 year after. Secondary, post-acute mortality and morbidity, health-related quality of life and healthcare utilisation will be investigated.Participants The MSC comprises adult (aged ≥18 years) patients who were treated for (severe) sepsis or septic shock on ICU. The participants were recruited between 15 April 2016 and 30 November 2018 from five German centres. Three thousand two hundred and ten patients with sepsis were identified, of which 1968 survived their ICU stay and were eligible for enrolment in the follow-up cohort. Informed consent for follow-up assessment was provided by 907 patients (46.1% of eligible patients).Findings to date The recruitment of the participants for follow-up assessments and the baseline data collection is completed. Incidence of sepsis was 116.7 patients per 1000 ICU patients. In this cohort profile, we provide an overview of the demographics and the clinical characteristics of both the overall sepsis cohort and the ICU survivors who provided informed consent for follow-up assessment (907 out of 1968 ICU survivors (46.1%)).Future plans The follow-ups are conducted 3, 6 and 12 months after ICU discharge. Another yearly follow-up up to 5 years after ICU discharge is pursued. Several cooperation and satellite projects were initiated. This prospective cohort offers a unique resource for research on long-term sequelae of sepsis survivors.Trial registration number German Clinical Trials Registry (DRKS00010050).

Background Patients surviving critical illnesses, such as sepsis, often suffer from long-term complications. After discharge from hospital, most patients are treated in primary care. Little is known how general practitioners (GPs) perform critical illness aftercare and how it can be improved. Within a randomised controlled trial, an outreach training programme has been developed and applied.Objectives The aim of this study is to describe GPs’ views and experiences of caring for postsepsis patients and of participating a specific outreach training.Design Semistructured qualitative interviews.Setting 14 primary care practices in the metropolitan area of Berlin, Germany.Participants 14 GPs who had participated in a structured sepsis aftercare programme in primary care.Results Themes identified in sepsis aftercare were: continuity of care and good relationship with patients, GP’s experiences during their patient’s critical illness and impact of persisting symptoms. An outreach education as part of the intervention was considered by the GPs to be acceptable, helpful to improve knowledge of the management of postintensive care complications and useful for sepsis aftercare in daily practice.Conclusions GPs provide continuity of care to patients surviving sepsis. Better communication at the intensive care unit–GP interface and training in management of long-term complications of sepsis may be helpful to improve sepsis aftercare.Trial registration number ISRCTN61744782.

BackgroundDuring the early phase of the Covid-19 pandemic, reductions of hospital admissions with a focus on emergencies have been observed for several medical and surgical conditions, while trend data during later stages of the pandemic are scarce. Consequently, this study aims to provide up-to-date hospitalization trends for several conditions including cardiovascular, psychiatry, oncology and surgery cases in both the in- and outpatient setting.Methods and findingsUsing claims data of 86 Helios hospitals in Germany, consecutive cases with an in- or outpatient hospital admission between March 13, 2020 (the begin of the "protection" stage of the German pandemic plan) and December 10, 2020 (end of study period) were analyzed and compared to a corresponding period covering the same weeks in 2019. Cause-specific hospitalizations were defined based on the primary discharge diagnosis according to International Statistical Classification of Diseases and Related Health Problems (ICD-10) or German procedure classification codes for cardiovascular, oncology, psychiatry and surgery cases. Cumulative hospitalization deficit was computed as the difference between the expected and observed cumulative admission number for every week in the study period, expressed as a percentage of the cumulative expected number. The expected admission number was defined as the weekly average during the control period. A total of 1,493,915 hospital admissions (723,364 during the study and 770,551 during the control period) were included. At the end of the study period, total cumulative hospitalization deficit was -10% [95% confidence interval -10; -10] for cardiovascular and -9% [-10; -9] for surgical cases, higher than -4% [-4; -3] in psychiatry and 4% [4; 4] in oncology cases. The utilization of inpatient care and subsequent hospitalization deficit was similar in trend with some variation in magnitude between cardiovascular (-12% [-13; -12]), psychiatry (-18% [-19; -17]), oncology (-7% [-8; -7]) and surgery cases (-11% [-11; -11]). Similarly, cardiovascular and surgical outpatient cases had a deficit of -5% [-6; -5] and -3% [-4; -3], respectively. This was in contrast to psychiatry (2% [1; 2]) and oncology cases (21% [20; 21]) that had a surplus in the outpatient sector. While in-hospital mortality, was higher during the Covid-19 pandemic in cardiovascular (3.9 vs. 3.5%, OR 1.10 [95% CI 1.06-1.15], PConclusionsThere have been varying changes in care pathways and in-hospital mortality in different disciplines during the Covid-19 pandemic in Germany. Despite all the inherent and well-known limitations of claims data use, this data may be used for health care surveillance as the pandemic continues worldwide. While this study provides an up-to-date analysis of utilization of hospital care in the largest German hospital network, short- and long-term consequences are unknown and deserve further studies.