Leonardo S.L. Bastos , Safira A. Wortel , Ferishta Bakhshi-Raiez , Ameen Abu-Hanna , Dave A. Dongelmans , Jorge I.F. Salluh , Fernando G. Zampieri , Gastón Burghi , Silvio Hamacher , Fernando A. Bozza , Nicolette F. de Keizer , Marcio Soares
{"title":"比较因果随机森林和线性回归来估算组织因素与重症监护室效率的独立联系","authors":"Leonardo S.L. Bastos , Safira A. Wortel , Ferishta Bakhshi-Raiez , Ameen Abu-Hanna , Dave A. Dongelmans , Jorge I.F. Salluh , Fernando G. Zampieri , Gastón Burghi , Silvio Hamacher , Fernando A. Bozza , Nicolette F. de Keizer , Marcio Soares","doi":"10.1016/j.ijmedinf.2024.105568","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.</p></div><div><h3>Methods</h3><p>A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF.</p></div><div><h3>Results</h3><p>The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: −0.13 [-0.24, −0.01] and −0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect.</p></div><div><h3>Conclusion</h3><p>In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.</p></div>","PeriodicalId":54950,"journal":{"name":"International Journal of Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency\",\"authors\":\"Leonardo S.L. Bastos , Safira A. Wortel , Ferishta Bakhshi-Raiez , Ameen Abu-Hanna , Dave A. Dongelmans , Jorge I.F. Salluh , Fernando G. Zampieri , Gastón Burghi , Silvio Hamacher , Fernando A. Bozza , Nicolette F. de Keizer , Marcio Soares\",\"doi\":\"10.1016/j.ijmedinf.2024.105568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.</p></div><div><h3>Methods</h3><p>A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF.</p></div><div><h3>Results</h3><p>The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: −0.13 [-0.24, −0.01] and −0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect.</p></div><div><h3>Conclusion</h3><p>In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.</p></div>\",\"PeriodicalId\":54950,\"journal\":{\"name\":\"International Journal of Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1386505624002314\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386505624002314","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency
Purpose
Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.
Methods
A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF.
Results
The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: −0.13 [-0.24, −0.01] and −0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect.
Conclusion
In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.
期刊介绍：
International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings.
The scope of journal covers:
Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.;
Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc.
Educational computer based programs pertaining to medical informatics or medicine in general;
Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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