Department of Clinical Pharmacy, Tergooi hospital, Hilversum, The Netherlands.
Julius Center for Health Sciences and Primary care, University Medical Centre Utrecht, Utrecht, The Netherlands.
PLoS One. 2019 Sep 27;14(9):e0218062. doi: 10.1371/journal.pone.0218062. eCollection 2019.
Observational studies have demonstrated that de-escalation of antimicrobial therapy is independently associated with lower mortality. This most probably results from confounding by indication. Reaching clinical stability is associated with the decision to de-escalate and with survival. However, studies rarely adjust for this confounder. We quantified the potential confounding effect of clinical stability on the estimated impact of de-escalation on mortality in patients with community-acquired pneumonia. Data were used from the Community-Acquired Pneumonia immunization Trial in Adults (CAPiTA). The primary outcome was 30-day mortality. We performed Cox proportional-hazards regression with de-escalation as time-dependent variable and adjusted for baseline characteristics using propensity scores. The potential impact of unmeasured confounding was quantified through simulating a variable representing clinical stability on day three, using data on prevalence and associations with mortality from the literature. Of 1,536 included patients, 257 (16.7%) were de-escalated, 123 (8.0%) were escalated and in 1156 (75.3%) the antibiotic spectrum remained unchanged. Crude 30-day mortality was 3.5% (9/257) and 10.9% (107/986) in the de-escalation and continuation groups, respectively. The adjusted hazard ratio of de-escalation for 30-day mortality (compared to patients with unchanged coverage), without adjustment for clinical stability, was 0.39 (95%CI: 0.19-0.79). If 90% to 100% of de-escalated patients were clinically stable on day three, the fully adjusted hazard ratio would be 0.56 (95%CI: 0.27-1.12) to 1.04 (95%CI: 0.49-2.23), respectively. The simulated confounder was substantially stronger than any of the baseline confounders in our dataset. Quantification of effects of de-escalation on patient outcomes without proper adjustment for clinical stability results in strong negative bias. This study suggests the effect of de-escalation on mortality needs further well-designed prospective research to determine effect size more accurately.
观察性研究表明，抗菌治疗降级与死亡率降低独立相关。这很可能是由于混杂因素的影响。达到临床稳定与降级决策和生存相关。然而，研究很少对此混杂因素进行调整。我们量化了临床稳定对社区获得性肺炎患者降级对死亡率估计影响的潜在混杂作用。数据来自成人社区获得性肺炎免疫试验（CAPiTA）。主要结局为 30 天死亡率。我们使用 Cox 比例风险回归，将降级作为时间依赖性变量，并使用倾向评分调整基线特征。通过使用文献中关于患病率和与死亡率相关性的数据，模拟代表第三天临床稳定的变量，来量化未测量混杂的潜在影响。在纳入的 1536 名患者中，257 名（16.7%）降级，123 名（8.0%）升级，1156 名（75.3%）抗生素谱不变。降级组和继续组的 30 天粗死亡率分别为 3.5%（9/257）和 10.9%（107/986）。未调整临床稳定时，降级 30 天死亡率的调整后危险比（与未改变覆盖范围的患者相比）为 0.39（95%CI：0.19-0.79）。如果 90%至 100%的降级患者在第三天达到临床稳定，完全调整后的危险比将分别为 0.56（95%CI：0.27-1.12）和 1.04（95%CI：0.49-2.23）。模拟的混杂因素比我们数据集中的任何基线混杂因素都要强得多。在没有适当调整临床稳定的情况下，量化降级对患者结局的影响会导致严重的负偏倚。本研究表明，降级对死亡率的影响需要进一步进行精心设计的前瞻性研究，以更准确地确定效应大小。
