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The epidemiology and volume-outcome relationship of extracorporeal membrane oxygenation for respiratory failure in Japan: A retrospective observational study using a national administrative database / 我が国における呼吸不全に対する体外式膜型人工肺(ECMO)の疫学とボリューム-アウトカム関係:全国的管理データベースを用いた後ろ向き観察研究Muguruma, Kohei 25 May 2020 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第22649号 / 社医博第109号 / 新制||社医||11(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 中山 健夫, 教授 川上 浩司, 教授 伊達 洋至 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM
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Impact of hospital volume on risk-adjusted mortality following oesophagectomy in Japan / 食道切除の病院あたりの手術件数とリスク調整死亡率との本邦における関連Nishigori, Tatsuto 23 March 2017 (has links)
京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20241号 / 医博第4200号 / 新制||医||1020(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 今中 雄一, 教授 川上 浩司, 教授 川村 孝 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM
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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, 2016–2018Roessler, Martin, Walther, Felix, Eberlein-Gonska, Maria, Scriba, Peter C., Kuhlen, Ralf, Schmitt, Jochen, Schoffer, Olaf 21 May 2024 (has links)
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.
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