Exploring relationships between in-hospital mortality and hospital case volume using random forest: results of a cohort study based on a nationwide sample of German hospitals, 2016-2018.

BACKGROUND: Relationships between in-hospital mortality and case volume were investigated for various patient groups in many empirical studies with mixed results. Typically, those studies relied on (semi-)parametric statistical models like logistic regression. Those models impose strong assumptions on the functional form of the relationship between outcome and case volume. The aim of this study was to determine associations between in-hospital mortality and hospital case volume using random forest as a flexible, nonparametric machine learning method. METHODS: We analyzed a sample of 753,895 hospital cases with stroke, myocardial infarction, ventilation > 24 h, COPD, pneumonia, and colorectal cancer undergoing colorectal resection treated in 233 German hospitals over the period 2016-2018. We derived partial dependence functions from random forest estimates capturing the relationship between the patient-specific probability of in-hospital death and hospital case volume for each of the six considered patient groups. RESULTS: Across all patient groups, the smallest hospital volumes were consistently related to the highest predicted probabilities of in-hospital death. We found strong relationships between in-hospital mortality and hospital case volume for hospitals treating a (very) small number of cases. Slightly higher case volumes were associated with substantially lower mortality. The estimated relationships between in-hospital mortality and case volume were nonlinear and nonmonotonic. CONCLUSION: Our analysis revealed strong relationships between in-hospital mortality and hospital case volume in hospitals treating a small number of cases. The nonlinearity and nonmonotonicity of the estimated relationships indicate that studies applying conventional statistical approaches like logistic regression should consider these relationships adequately.

Patient-Level and Hospital-Level Risk Factors for In-Hospital Mortality in Patients Ventilated for More Than 24 Hours: Results of a Nationwide Cohort Study.

BACKGROUND: Prolonged ventilation is associated with a high risk of death. This study investigated both patient-level and hospital-level risk factors for in-hospital mortality in patients ventilated for more than 24 hours. METHODS: The analyses were conducted in the framework of a German national multicenter retrospective cohort study. Patient and hospital characteristics were examined using descriptive statistics. Risk factors of in-hospital mortality were analyzed using multilevel robust Poisson regressions for binary outcomes. Potential effect modifications were examined by stratified analyses. RESULTS: The sample includes 95 672 cases of patients ventilated >24 hours in 163 hospitals covering the period 2016 to 2017. According to the results of multilevel Poisson regressions, main patient-level risk factors for in-hospital mortality were age (per year relative risk [RR] = 1.021, 95% CI = 1.020-1.023), stroke (RR = 1.459; 95% CI = 1.361-1.563), emergency case admission (RR = 1.273, 95% CI = 1.156-1.403), and transfer from another hospital (RR = 1.169, 95% CI = 1.084-1.261). The individual risk of in-hospital death was positively associated with hospital size (RR of hospitals with 600-799 beds vs <100 beds = 1.412, 95% CI = 1.095-1.820) and negatively related to cumulated ventilation patient time (per 1000 days RR = 0.995, 95% CI = 0.993-0.996). University hospital status was identified as an effect modifier, particularly with regard to the patients' admission reasons. The RR of in-hospital death in patients admitted after transfer from another hospital was higher in university hospitals (RR = 1.456, 95% CI = 1.298-1.634) compared to nonuniversity hospitals (RR = 1.077, 95% CI = 1.019-1.139). Likewise, patients admitted as emergency case had a higher relative risk in university hospitals (RR = 1.619, 95% CI = 1.359-1.929) than in nonuniversity hospitals (RR = 1.141, 95% CI = 1.080-1.205). CONCLUSION: By providing evidence on multiple patient-level and hospital-level risk factors for in-hospital mortality in patients ventilated for more than 24 hours, this large multicenter study has main implications for quality assessment of clinical care and the adequate specification of risk adjustment models. The revealed effect modifications indicate the relevance of stratified analyses.

[Benchmarking and peer reviewing with routine data, using the example of IQM (Initiative Qualitätsmedizin e.V.)]. / Benchmarking und Peer Review unter Verwendung von Routinedaten am Beispiel der IQM Initiative Qualitätsmedizin e.V.

IQM is a non-profit association of hospitals and open to all kinds of hospitals. IQM aims to enhance the management of quality improvement using an innovative und efficient method including:

[Health services research: innovation transfer in clinical research]. / Versorgungsforschung: Innovationstransfer in der Klinischen Forschung.

Clinical research in Germany suffers from a structural crisis caused by inappropriate input, suboptimal output and low efficiency. The situation is aggravated by fixed DRG-based payments, fix charges for education and research and reductions in governmental funding. Both healthcare system and clinical research in Germany are characterized by a profound sectorization, followed by resource competition between basic science and patient-oriented research. Basic biomedical research in Germany is widely accepted in the international context, but there is a lack of research on the transfer of basic science to clinical trials and everyday healthcare. According to international examples, the 1999 systematology of the Deutsche Forschungs-Gemeinschaft (DFG), a German research foundation, has to be expanded to the dimension of innovation transfer. Clinical translational research focuses on the transfer of basic science to clinical trials, and outcomes research, also known as health services research, describes the transfer of results from clinical trials (efficacy) to clinical application (effectiveness). Outcomes research is interdisciplinary, multiprofessional and patient-oriented. The so-called effectiveness gap between efficacy and effectiveness is determined by patient-sided, professional, organizational and system factors. Outcomes research provides university-based medical research centers with the opportunity to join other stakeholders of the healthcare system and to integrate the universities' scientific standard with its own further development. It is suggested that this widened perspective plays a key role in settling the crisis of clinical research in Germany.