Tyler J. Loftus, M. Ruppert, B. Shickel, T. Ozrazgat-Baslanti, Jeremy A. Balch, Kenneth L. Abbott, Die Hu, Adnan Javed, Firas G Madbak, F. Guirgis, David Skarupa, P. Efron, P. Tighe, William R. Hogan, Parisa Rashidi, Gilbert R. Upchurch, A. Bihorac
{"title":"社会人口因素与大手术后护理过度、护理不足和护理价值的关系","authors":"Tyler J. Loftus, M. Ruppert, B. Shickel, T. Ozrazgat-Baslanti, Jeremy A. Balch, Kenneth L. Abbott, Die Hu, Adnan Javed, Firas G Madbak, F. Guirgis, David Skarupa, P. Efron, P. Tighe, William R. Hogan, Parisa Rashidi, Gilbert R. Upchurch, A. Bihorac","doi":"10.1097/as9.0000000000000429","DOIUrl":null,"url":null,"abstract":"\n \n To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.\n \n \n \n In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.\n \n \n \n This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.\n \n \n \n Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K–$23.5K) vs $14.1K ($9.1K–$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.\n \n \n \n Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.\n","PeriodicalId":503165,"journal":{"name":"Annals of Surgery Open","volume":"54 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery\",\"authors\":\"Tyler J. Loftus, M. Ruppert, B. Shickel, T. Ozrazgat-Baslanti, Jeremy A. Balch, Kenneth L. Abbott, Die Hu, Adnan Javed, Firas G Madbak, F. Guirgis, David Skarupa, P. Efron, P. Tighe, William R. Hogan, Parisa Rashidi, Gilbert R. Upchurch, A. Bihorac\",\"doi\":\"10.1097/as9.0000000000000429\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.\\n \\n \\n \\n In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.\\n \\n \\n \\n This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.\\n \\n \\n \\n Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K–$23.5K) vs $14.1K ($9.1K–$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. 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引用次数: 0
摘要
目的是确定某些病人是否容易在大手术后立即受到错误分流决定的影响,以及在过度分流和分流不足的人群中是否存在独特的社会人口表型。 在一个公平的系统中,将低急症患者过度分流到重症监护室(ICU)和将高急症患者分流到普通病房会对所有社会人口亚群产生同等影响。 这项针对大手术后立即入院的多中心纵向队列研究比较了 4 个队列的住院死亡率和护理价值(风险调整后死亡率/总成本),这 4 个队列分别是:过度分流(660 人)、风险匹配的普通病房过度分流对照组(3077 人)、过度分流(2335 人)和风险匹配的重症监护病房过度分流对照组(4774 人)。K-均值聚类确定了过度接种和低度接种队列中的社会人口表型。 与对照组相比,过度分流的入院患者中男性患者居多(56.2% vs 43.1%,P < 0.001),且有商业保险(6.4% vs 2.5%,P < 0.001);分流不足的入院患者中黑人患者居多(28.4% vs 24.4%,P < 0.001),且社会经济贫困程度更高。过度接诊与直接总成本增加[1.62万美元(11.4-23.5万美元)vs 1.41万美元(9.1-20.7万美元),P < 0.001]和护理价值低有关;不足接诊与住院死亡率增加(1.5% vs 0.7%,P = 0.002)和临终关怀(2.2% vs 0.6%,P < 0.001)和护理价值低有关。过度分流和过度分流队列中的独特社会人口表型具有相似的治疗效果和护理价值,这表明分流决策而非患者特征决定了治疗效果和护理价值。 术后分流决策应确保不同社会人口群体之间的平等,将分流决策与客观的患者严重程度评估挂钩,避免认知上的捷径并减少偏见。
Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery
To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts.
In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally.
This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts.
Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K–$23.5K) vs $14.1K ($9.1K–$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care.
Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.