Skip to main content
Download PDF

Haloperidol in treating delirium, reducing mortality, and preventing delirium occurrence: Bayesian and frequentist meta-analyses

Critical Care volume 29, Article number: 126 (2025) Cite this article

Matters Arising to this article was published on 23 April 2025

Abstract

Background

Although haloperidol is commonly used to treat or prevent delirium in intensive care unit (ICU) patients, the evidence remains inconclusive. This study aimed to comprehensively evaluate the efficacy and safety of haloperidol for delirium treatment and prevention in ICU patients.

Methods

We searched MEDLINE, the cochrane central register of controlled trials, EMBASE, ClinicalTrial.gov, and PubMed without language restrictions from database inception to June 27, 2024. We included double-blind randomized controlled trials (RCTs) on haloperidol versus placebo for treating and preventing delirium in adult ICU patients. In addition to frequentist analyses, Bayesian analysis was used to calculate the posterior probabilities of any benefit/harm and clinically important benefit/harm (CIB/CIH). The primary outcomes for delirium treatment were all-cause mortality and serious adverse events (SAEs). For delirium prevention, the primary outcomes included incident delirium, all-cause mortality, and SAEs. The secondary outcomes for efficacy were delirium-or coma-free days, ventilator-free days, length of stay in ICU, length of stay in hospital, and rescue benzodiazepine use. The secondary outcomes for safety were QTc prolongation and extrapyramidal syndrome.

Results

We included seven RCTs on delirium treatment (n = 1767) and five on delirium prevention (n = 2509). The Bayesian analysis showed that, compared to placebo for delirium treatment, haloperidol had a 68% probability of achieving CIB (defined as risk difference [RD] < −0.02) in reducing all-cause mortality, a 2% probability of achieving CIH (RD > 0.02) in causing SAEs, and a 78% probability of achieving CIB (RD < −0.02) in reducing the need for rescue benzodiazepine use. The probabilities of haloperidol causing CIH (RD > 0.02) across all other safety outcomes were low (all < 50%). In frequentist analysis on delirium treatment, the pooled estimated RD for haloperidol compared to placebo was -0.05 (−0.09, −0.00; I2 = 0%) for rescue benzodiazepine use. In Bayesian analysis on delirium prevention, haloperidol had a 12% probability of achieving CIB in all-cause mortality, a 34% probability of achieving CIB in delirium incidence, and a 0% probability of achieving CIB in SAEs. Importantly, haloperidol had a 65% probability of causing CIH (risk ratio > 1.1) for QTc prolongation, while the posterior probabilities of achieving CIB across all efficacy outcomes were low (all < 50%). In frequentist analysis on delirium prevention, all primary and secondary outcomes were not statistically significant in frequentist analysis.

Conclusion

Our study supported the use of haloperidol for delirium treatment in adult ICU patients, but not for delirium prevention.

Introduction

Delirium is a prevalent, serious, and potentially fatal acute disturbance of attention and cognition [1, 2]. It affects 30–60% of intensive care unit (ICU) patients on mechanical ventilation and 20–40% of non-ventilated patients [2,3,4,5,6]. Several adverse outcomes have been reported in patients with delirium, including long-term cognitive impairment, longer ventilator use, extended ICU and hospital stays, increased benzodiazepine (BZD) use, impaired daily activity, and higher mortality rates [3, 7,8,9]. Therefore, there is a critical need to identify effective methods for treating or preventing delirium, ensuring optimal outcomes for ICU patients [6].

Previous studies have examined several pharmacological interventions for delirium management, including haloperidol, dexmedetomidine, ramelteon, and rivastigmine [10, 11]. Although BZDs are commonly used to treat agitation in patients with delirium, such medications have been associated with exacerbating delirium symptoms and increasing mortality [9, 12]. Generally, delirium signals a patient is at heightened risk, but no proven pharmacological interventions to treat delirium exist [13].

The largest RCT (n = 1000) found no significant difference in days alive or out of the hospital at 90 days between haloperidol and placebo. However, a Bayesian re-analysis suggested high probabilities of benefit and low probabilities of harm for haloperidol across outcomes [14, 15]. Importantly, most meta-analyses have used a frequentist framework to synthesize evidence from RCTs, but their findings have been inconsistent [16, 17]. As placebo-controlled RCTs are published, the frequentist meta-analysis can be updated. In addition, to date, no Bayesian meta-analysis has addressed the efficacy and safety of haloperidol in the treatment or prevention of delirium.

To address this knowledge gap, we employed both Bayesian and frequentist meta-analyses to simultaneously evaluate haloperidol for both the treatment and prevention of delirium. Importantly, Bayesian analysis uses probabilities instead of dichotomized conclusions, as seen in frequentist statistics, providing a more nuanced and probabilistic approach to interpreting data. Moreover, we determined the probabilities of any benefit/harm, as well as clinically important benefits/harms (CIBs/CIHs), for key delirium-related outcomes. This dual-method approach can provide more comprehensive results and compensates for the shortcomings of each method. The frequentist method provides traditional effect size estimates and significance testing, while the Bayesian method offers more flexible probabilistic inference and quantification of uncertainty, thereby enhancing the confidence and value of the meta-analysis for physicians regarding the nuanced aspects of haloperidol’s efficacy and safety in the treatment and prevention of delirium [18].

Methods

The study protocol was registered with PROSPERO (CRD42024562120). We followed the Preferred Reporting Items for Systematic Reviews and Meta­analyses (PRISMA) [19], which can be found in Appendix 1.

Eligibility criteria

Eligible studies were double-blinded, randomized controlled trials with a placebo control. We included (i) adult ICU patients (age ≥ 18 years), (ii) adults evaluated for delirium treatment or prevention. We excluded (i) conference abstracts, editorials, case series, observational studies, single-arm pre–post studies, head-to-head design without placebo, and quasi-randomized trials, and (ii) non-ICU patients, patients aged less than 18 years, or studies involving combination therapy with other medication. The route and dosage of haloperidol were not restricted, and rescue medication was allowed.

Data sources and search

Two reviewers independently searched MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE, ClinicalTrial.gov, and PubMed without language restrictions from database inception to June 27, 2024. We also searched the gray literature and reviewed the reference lists of the included studies and related systematic reviews [16, 17].

Study selection

Two reviewers independently screened titles, abstracts, and full-text articles. Disagreements were resolved through discussion and, if necessary, by consulting the corresponding authors. Appendix 2 demonstrates the complete search strategies and appendix 3 shows the reasons for exclusion.

Data extraction and outcome definition

Two reviewers independently extracted the data and discrepancies were resolved by consensus and, when necessary, by consulting the corresponding authors. The primary outcomes of delirium treatment were all-cause mortality and serious adverse event (SAE), whereas the secondary outcomes were delirium- or coma-free days (DCFD), ventilator-free days (VFD), rescue BZD use, length of stay in ICU days, length of stay in hospital days, extrapyramidal syndrome (EPS), and QTc prolongation.

The primary outcomes of delirium prevention were all-cause mortality during study period at longest follow-up, incident delirium, and SAE. The secondary outcomes were length of stay in ICU days, length of stay in hospital days, rescue BZD use, EPS, and QTc prolongation. WebPlot Digitizer (https://apps.automeris.io/wpd/) was used to extract numerical data from the figures. We assessed all outcomes at the maximum follow-up, including all-cause mortality. SAE is defined as an untoward medical occurrence that: (1) Is life-threatening, (2) Requires hospitalization or prolongation of existing hospitalization, (3) Results in persistent or significant disability, or (4) Is considered an important medical event based on medical or scientific judgment. We excluded mortality when calculating SAE. SAE events will only be extracted when they are proactively reported in the study.

Quality assessment

The Cochrane Risk of Bias Assessment Tool (version 2.0) was utilized to evaluate the quality of the RCTs [20]. Two authors independently conducted this assessment, and discrepancies were resolved by consulting the corresponding authors.

Data synthesis

In the Bayesian framework, a generalized linear mixed model was applied. For dichotomous outcomes (e.g., SAE), we calculated the risk difference (RD) and risk ratio (RR), and for continuous outcomes (e.g., length of stay in ICU days), we calculated the mean difference (MD) and ratio of means (ROM). For simplicity, we primarily reported RD and MD. Pooled estimates were calculated with 95% credible intervals (CrI), and quantitative heterogeneity was assessed using the posterior estimates of the heterogeneity parameter (Ï„) with its 95% CrI. Bayesian meta-analysis provides posterior probabilities that both the primary and secondary outcomes achieve clinically important benefit (CIB) and clinically important harm (CIH). The definitions of thresholds [15] for any benefit, harm, CIB, and CIH were pre-specified in the protocol (see eTable 1).

Except for DCFD and VFD, the cut-off values were defined as follows:

Any benefit: RD < 0, MD < 0, RR < 1, and ROM < 1.

Any harm: RD > 0, MD > 0, RR > 1, and ROM > 1.

CIB: RD < −0.02 (decrease 2%), MD < −1, RR < 0.9, and ROM < 0.9

CIH: RD > 0.02 (increase 2%), MD > 1, RR > 1.1, and ROM > 1.1

For DCFD and VFD, the cut-off values were defined as follows:

Any benefit: MD > 0, ROM > 1.

Any harm: MD < 0, ROM < 1.

CIB: MD > 1, ROM > 1.1

CIH: MD < −1, ROM < 0.9

A Bayesian multilevel model using Stan was performed. Appendix 4 presents the convergence diagnostics and prior distributions. We used weakly informative priors in the Bayesian analyses of all outcomes. These priors were centered on no difference and included all plausible effect sizes to ensure that the data predominantly informed the results while preventing extreme inferences. The prior distributions were selected based on their suitability for each outcome type, with normal distributions for intercepts and Cauchy distributions for heterogeneity parameters, both parameterized to allow for reasonable uncertainty while avoiding overly strong assumptions. For example, for risk difference outcomes, we specified a Normal prior for the intercept (location = 0, scale = 0.2), with 95% of the prior probability mass within [−0.39, 0.39], and a Cauchy prior for heterogeneity (location = 0, scale = 0.2), with 95% probability mass within [−2.54, 2.54]. Four Markov chains are implemented. There were 50,000 iterations per chain, and the first 20,000 iterations for each chain were discarded as warm-up. Convergence was assessed by visual inspection of the trace and autocorrelation plots of the key parameters for each analysis. For both delirium treatment and prevention, a sensitivity analysis using a beta-binomial model was conducted for the primary outcomes within the Bayesian analysis. The beta-binomial model allows for more flexible modeling of between-study variability in the event rates, particularly when there is uncertainty in the event probabilities.

In the frequentist framework, a random-effects meta-analysis was conducted using restricted maximum likelihood. Pooled estimates were reported with 95% confidence intervals (CI), and quantitative heterogeneity was assessed using the I2 statistic, and an I2 > 50% indicates substantial heterogeneity between studies. A continuity correction of 0.5 was applied for zero events in one arm, while studies with zero events in both arms were excluded from the analysis. All analyses were conducted using the statistical software R, version 4.2.0 (R Core Team, R Foundation for Statistical Computing, Vienna, Austria) with the brms, meta, rjags, and metafor R packages [21,22,23,24].

Results

After completing the systematic search and screening procedures, 82 articles were considered for full-text review, of which 12 met the eligibility criteria (Fig. 1). Among the 12 studies, 7 were on delirium treatment [14, 25,26,27,28,29,30] and 5 were on delirium prevention [31,32,33,34,35]. In the RCTs on delirium treatment, one study [29] reported that 90% of participants exhibited delirium on day 1, and another study [27] included participants who had abnormal levels of consciousness, and 83% of participants experienced delirium or coma on day 1. Therefore, these two studies were appropriately included in the treatment of delirium category. Among the RCTs on delirium prevention, one trial included patients with subsyndromal delirium [32], while the remaining trials involved ICU patients without delirium.

Fig. 1
figure 1

Flow chart of study selection. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.n7

Full size image

Demographic characteristics

Overall, 1767 participants (mean age: 66.87 years [standard deviation: 5.25]; average male percentage range: 53.3–68.5%) were included for delirium treatment, and 2509 participants (mean age: 67.20 years [4.42]; average male percentage range: 55.9–74.4%) were included for delirium prevention. The details of the characteristics of the included studies are presented in eTable 2.

Delirium treatment

Figure 2 presents the primary outcomes of delirium treatment using Bayesian meta-analysis, while Fig. 3 presents the results of frequentist meta-analysis. For all-cause mortality, we found four trials (4/7) with low ROB (eFigs. 1 and 10). In Bayesian analysis, haloperidol had an RD of −0.03 (95% CrI: −0.09, 0.03) compared with placebo. We found an 86% probability of any benefit of haloperidol and a 68% probability of CIB of haloperidol. In frequentist analysis (Fig. 3A, B), no statistically significant difference was found (RD: −0.03, 95% CI: −0.08, 0.01, I2 = 0%).

Fig. 2
figure 2

Posterior probability of haloperidol versus placebo for delirium treatment on the primary outcomes. The distribution chart on the right, yellow represents the probability of exceeding the clinical important benefit, while pink represents the probability of being below the clinical important benefit. For the values on the left, yellow represents the benefit values, and pink represents the harm values. Abbreviation: CIB = clnically imporant benefit; CIH = clinically imporant harm. aDotted line indicates clinical important benefit, and solid line indicates no difference. bThe cut-off point for clinically important benefit is −0.02 for risk difference and 0.9 for risk ratio, while the cut-off point for clinically important harm is 0.02 for risk difference and 1.1 for risk ratio

Full size image
Fig. 3
figure 3

Haloperidol for delirium treatment using frequentist meta-analysis. Dichotomous outcomes of delirium treatment presented as A Risk ratio B Risk difference. Continuous outcomes of delirium treatment presented as (C) mean difference (day) (B) Ratio of means. Abbreviation: CI = confidence interval; ICU = intensive care unit; MD = mean difference; RD = risk difference; RoM = ratio of means; RR = Risk ratio. aInterpret grey-shaded results according to the grey-shaded directional indications

Full size image

For SAEs, we found four trials (4/6) with low ROB (eFigs. 2 and 11). In Bayesian analysis, haloperidol had an RD of −0.01 (95% CrI: −0.03, 0.02) compared with placebo. We found only a 2% probability of CIH of haloperidol. In frequentist analysis (Fig. 3A, B), no statistically significant difference was found (RD: −0.01, 95% CI: −0.02, 0.01, I2 = 0%).

The secondary outcomes are presented in Table 1 and Fig. 3, namely rescue BZD use, EPS, QTc prolongation, DCFD, VFD, length of stay in ICU days, and length of stay in hospital days. Few studies reported on secondary outcomes had low ROB (eFigs. 3–9, 12–18). In Bayesian analysis, haloperidol had an RD of −0.05 (95% CrI: −0.14, 0.04) for rescue BZD use compared with placebo. We found a 90% probability of any benefit and a 78% probability of CIB for haloperidol in reducing rescue BZD use. For the other secondary outcomes, the probabilities of CIB and CIH were low (all < 50%). In frequentist analysis, no statistically significant differences were found between the haloperidol and placebo in most secondary outcomes. However, for rescue BZD use, haloperidol was associated with a lower rescue BZD use compared with placebo (an RD of −0.05, 95% CI: −0.09, −0.00; I2 = 0%).

Table 1 Posterior probability of haloperidol for delirium treatment for the secondary outcomes
Full size table

Delirium prevention

Table 2 presents the primary and secondary outcomes of delirium prevention using Bayesian meta-analysis, while eFig. 35 presents the results of frequentist meta-analysis. For all-cause mortality, we found three trials (3/5) with low ROB (eFigs. 19 and 27). In Bayesian analysis, haloperidol had an RD of −0.01 (95% CrI: −0.03, 0.02) with a 12% probability of achieving CIB. For delirium incidence, we found three trials (3/5) with low ROB (eFigs. 20 and 28). Haloperidol had an RD of −0.01 (95% CrI: −0.09, 0.08) with a 34% probability of achieving CIB. For SAEs, we found three trials (3/5) with low ROB (eFigs. 21 and 29). Haloperidol had an RD of 0.00 (95% CrI: −0.01, 0.01) with a 0% probability of achieving CIB. In frequentist analysis, there were no statistically significant differences between haloperidol and placebo in all primary outcomes.

Table 2 Posterior probability of haloperidol for delirium prevention for the primary and the secondary outcomes
Full size table

For secondary outcomes, namely rescue BZD use, EPS, QTc prolongation, length of stay in ICU days, and length of stay in hospital days, the quality assessment showed few studies had low ROB (eFig. 22–25, 30–33). In Bayesian analysis, the probabilities of CIB and CIH for all secondary outcomes were low (all < 50%). Notably, haloperidol had an RR of 1.21 (95% CrI: 0.70, 2.16) for QTc prolongation compared with placebo, with a 65% probability of CIH. In frequentist analysis, there were no statistically significant differences between the haloperidol and placebo in all secondary outcomes.

The forest plots for each outcome using the frequentist analysis (eFigs. 36–69) and the Bayesian analysis (eFigs. 70–103) are included in the supplementary data.

Sensitivity analyses

Finally, the results of sensitivity analysis of beta-binomial model showed similar findings on the primary outcomes of delirium treatment and prevention (eTable 3).

Discussion

Principal findings

For delirium treatment with haloperidol versus placebo, we found moderate posterior probabilities of achieving CIB on all-cause mortality and low posterior probabilities of causing SAEs. In addition, we also found a high posterior probability of achieving CIB on rescue BZD use. Regarding other safety outcomes, we found low posterior probabilities of causing CIH. When using haloperidol for delirium prevention, we found low posterior probability of achieving CIB on all-cause mortality, delirium incidence, and SAEs.

However, we found a high posterior probability of causing CIH on QTc prolongation. Given the high probability of any benefit on mortality, low probability of harm, and high probability of less rescue BZD use, the use of haloperidol for treatment of delirium was supported. However, the efficacy and safety of prophylactic haloperidol for delirium prevention is not supported by the findings of this study.

Comparison with other studies

In our study, the Bayesian analysis suggested that haloperidol for delirium treatment in ICU patients had high probabilities and moderate probabilities of achieving any benefits and CIB on all-cause mortality. A previous meta-analysis with five placebo-controlled RCTs [16] favored haloperidol in decreasing all-cause mortality, although the results were not statistically significant. Compared with this meta-analysis [16], we included two additional placebo-controlled RCTs [27, 29]. The mechanisms of mortality in ICU patients with delirium are complex and multifaceted; thus, several potential reasons for haloperidol-associated lower mortality need to be considered. At the molecular level, in vitro studies have demonstrated the anti-inflammatory effects of haloperidol [36, 37]. This effect may mitigate the cytokine storm in critically ill patients, potentially antagonizing multiple pathways of delirium and reducing the risk of mortality [34, 38]. Second, haloperidol was associated with reduced BZD use in ICU patients with delirium. Although BZDs can be used to manage agitation, anxiety, and insomnia, they may exacerbate delirium and increase mortality among ICU patients with delirium [9, 12]. Therefore, haloperidol might have resulted from less rescue BZD administration. Third, we also found an approximately 80% probability for any benefit of haloperidol versus placebo for the outcome of DCFD. Previous studies have shown that the delirium duration is a strong predictor of 30-day and 2-year death [39, 40].

Results of both frequentist and Bayesian analyses did not support the use of prophylactic haloperidol in ICU patients to prevent major delirium-related outcomes, including all-cause mortality, incident delirium, and SAEs. These results aligned with the findings of a previous meta-analysis within the frequentist framework [41]. Prophylactic haloperidol for delirium prevention did not achieve CIB with at least more than 50% posterior probability for any of the outcomes we measured. Importantly, prophylactic haloperidol for delirium prevention demonstrated a 65% posterior probability of causing CIH on QTc prolongation.

Our study showed that, at the population level, there was no significant difference in the risk of SAE, EPS, and QTc prolongation among ICU patients treated with haloperidol compared with those treated with placebo. These findings were consistent with those of a previous meta-analysis [41]. A systematic review of 34 clinical trials indicated that individuals who received cumulative doses of at least 100 mg haloperidol intravenously or exhibited a QTc interval > 500 ms were considered at a relatively higher risk of QTc prolongation [42]. In our study, the majority of the included RCTs utilized intravenous haloperidol at relatively lower doses, ranging from 1.5 to 7.5 mg/day regularly, or with a maximum of 24 mg/day [14, 26, 28,29,30,31,32,33,34,35]. Studies involving oral or intramuscular routes did not exceed 30 mg/day [25, 27]. Indeed, in our study, neither frequentist nor Bayesian analyses suggested that haloperidol was associated with a higher risk of EPS than that of the placebo.

Limitations

The present study had several limitations. First, there are few studies we included with low risk of bias. Second, the results are limited by few patients making the estimates uncertain. Furthermore, owing to the limited number of studies included, meta-regression and subgroup analyses were not performed. Third, the rescue medications used in different RCTs varied, including not only BZD but also other medication such as antipsychotics, fentanyl, propofol, clonidine, morphine, and dexmedetomidine. With the limited available data, we could only evaluate the risk of rescue BZD use. However, these rescue medications could potentially affect outcomes. I would also include that many studies allow rescue haloperidol meaning diluting a possible potential effect. For example, 20% of patients received open-label antipsychotics in the MIND-USA trial [26], meaning a potential difference could be difficult to find if 1 of 4 in the placebo group might have received haloperidol. Fourth, variations in the administration route and haloperidol dose, as well as heterogeneity in the characteristics of ICU patients (such as underlying diseases, medical/surgical status, ventilator usage, initial severity of delirium, and age), may impede the comparability of outcomes. Fifth, we only included RCTs with critically ill patients in the ICU; therefore, our results cannot be extrapolated to patients with less severe disease. Sixth, there are two RCTs [27, 29] considered as treatment trials, in which only 90% and 83% of participants, respectively, had delirium at the start of the trial. Thus, the results of treatment could be mild overestimated. Seventh, since we are unable to determine the length of hospital stay for the deceased cases with the limited available data, the hospital/ICU stay outcomes may be influenced by these mortality cases (for example, if patients die shortly after being admitted to the ICU, it would reduce the average ICU stay duration). Eighth, two studies did not provide tools for assessing delirium, and both were evaluated as having a high risk of bias because they had more than three domains rated as "some concerns." Lastly, when there is significant heterogeneity between studies, the results of Bayesian analysis may be greatly affected [18].

Implications and conclusions

Delirium is common, lethal, and expensive condition occurring in ICU patients. Our study provides insights to physicians regarding the nuanced aspects of haloperidol efficacy and safety in this critically ill population. For clinical practice, we suggest that, at the population level, haloperidol for delirium treatment is associated with high probabilities of achieving any benefit on all-cause mortality, moderate probabilities of achieving CIB for these outcomes, high probabilities of achieving CIB for rescue BZD use, and low probabilities of causing CIH across all safety outcomes. It can be used on patients with delirium within a reasonable dosage range. However, there is no robust evidence to support the use of prophylactic haloperidol for delirium prevention. Future studies are required to investigate the predictive factors of both benefit and harm in relation to delirium-related outcomes. In addition, the homogeneity of participants can be increased, such as including only heart surgery patients, stroke patients, or pneumonia patients.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Inouye SK, Westendorp RG, Saczynski JS. Delirium in elderly people. Lancet. 2014;383(9920):911–22.

    Article  PubMed  Google Scholar 

  2. Krewulak KD, Stelfox HT, Leigh JP, Ely EW, Fiest KM. Incidence and prevalence of delirium subtypes in an adult ICU: a systematic review and meta-analysis. Crit Care Med. 2018;46(12):2029–35.

    Article  PubMed  Google Scholar 

  3. Brummel NE, Jackson JC, Pandharipande PP, Thompson JL, Shintani AK, Dittus RS, Gill TM, Bernard GR, Ely EW, Girard TD. Delirium in the ICU and subsequent long-term disability among survivors of mechanical ventilation. Crit Care Med. 2014;42(2):369–77.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Ely EW, Inouye SK, Bernard GR, Gordon S, Francis J, May L, Truman B, Speroff T, Gautam S, Margolin R, et al. Delirium in mechanically ventilated patients: validity and reliability of the confusion assessment method for the intensive care unit (CAM-ICU). JAMA. 2001;286(21):2703–10.

    Article  CAS  PubMed  Google Scholar 

  5. Ely EW, Margolin R, Francis J, May L, Truman B, Dittus R, Speroff T, Gautam S, Bernard GR, Inouye SK. Evaluation of delirium in critically ill patients: validation of the confusion assessment method for the intensive care unit (CAM-ICU). Crit Care Med. 2001;29(7):1370–9.

    Article  CAS  PubMed  Google Scholar 

  6. McNicoll L, Pisani MA, Zhang Y, Ely EW, Siegel MD, Inouye SK. Delirium in the intensive care unit: occurrence and clinical course in older patients. J Am Geriatr Soc. 2003;51(5):591–8.

    Article  PubMed  Google Scholar 

  7. Pandharipande PP, Girard TD, Jackson JC, Morandi A, Thompson JL, Pun BT, Brummel NE, Hughes CG, Vasilevskis EE, Shintani AK, et al. Long-term cognitive impairment after critical illness. N Engl J Med. 2013;369(14):1306–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Salluh JI, Wang H, Schneider EB, Nagaraja N, Yenokyan G, Damluji A, Serafim RB, Stevens RD. Outcome of delirium in critically ill patients: systematic review and meta-analysis. BMJ. 2015;350:h2538.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Senderovich H, Gardner S, Berall A, Ganion M, Zhang D, Vinoraj D, Waicus S. Benzodiazepine use and morbidity-mortality outcomes in a geriatric palliative care unit: a retrospective review. Dement Geriatr Cogn Disord. 2021;50(6):559–67.

    Article  CAS  PubMed  Google Scholar 

  10. Wu YC, Tseng PT, Tu YK, Hsu CY, Liang CS, Yeh TC, Chen TY, Chu CS, Matsuoka YJ, Stubbs B, et al. Association of delirium response and safety of pharmacological interventions for the management and prevention of delirium: a network meta-analysis. JAMA Psychiat. 2019;76(5):526–35.

    Article  Google Scholar 

  11. Yu CL, Carvalho AF, Thompson T, Tsai TC, Tseng PT, Tu YK, Yang SN, Yang FC, Chang CH, Hsu CW, et al. Ramelteon for delirium prevention in hospitalized patients: an updated meta-analysis and trial sequential analysis of randomized controlled trials. J Pineal Res. 2023;74(3):e12857.

    Article  CAS  PubMed  Google Scholar 

  12. Vasilevskis EE, Han JH, Hughes CG, Ely EW. Epidemiology and risk factors for delirium across hospital settings. Best Pract Res Clin Anaesthesiol. 2012;26(3):277–87.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Marra A, Kotfis K, Hosie A, MacLullich AMJ, Pandharipande PP, Ely EW, Pun BT. Delirium monitoring: yes or no? That is the question. Am J Crit Care. 2019;28(2):127–35.

    Article  PubMed  Google Scholar 

  14. Andersen-Ranberg NC, Poulsen LM, Perner A, Wetterslev J, Estrup S, Hastbacka J, Morgan M, Citerio G, Caballero J, Lange T, et al. Haloperidol for the treatment of delirium in ICU patients. N Engl J Med. 2022;387(26):2425–35.

    Article  CAS  PubMed  Google Scholar 

  15. Andersen-Ranberg NC, Poulsen LM, Perner A, Hastbacka J, Morgan M, Citerio G, Collet MO, Weber SO, Andreasen AS, Bestle M, et al. Haloperidol vs. placebo for the treatment of delirium in ICU patients: a pre-planned, secondary Bayesian analysis of the AID-ICU trial. Intensive Care Med. 2023;49(4):411–20.

    Article  CAS  PubMed  Google Scholar 

  16. Andersen-Ranberg NC, Barbateskovic M, Perner A, Oxenboll Collet M, Musaeus Poulsen L, van der Jagt M, Smit L, Wetterslev J, Mathiesen O, Maagaard M. Haloperidol for the treatment of delirium in critically ill patients: an updated systematic review with meta-analysis and trial sequential analysis. Crit Care. 2023;27(1):329.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Barbateskovic M, Krauss SR, Collet MO, Andersen-Ranberg NC, Mathiesen O, Jakobsen JC, Perner A, Wetterslev J. Haloperidol for the treatment of delirium in critically ill patients: a systematic review with meta-analysis and trial sequential analysis. Acta Anaesthesiol Scand. 2020;64(2):254–66.

    Article  PubMed  Google Scholar 

  18. Fornacon-Wood I, Mistry H, Johnson-Hart C, Faivre-Finn C, O’Connor JPB, Price GJ. Understanding the differences between Bayesian and frequentist statistics. Int J Radiat Oncol Biol Phys. 2022;112(5):1076–82.

    Article  PubMed  Google Scholar 

  19. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sterne JAC, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng HY, Corbett MS, Eldridge SM, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898.

    Article  PubMed  Google Scholar 

  21. Balduzzi S, Rücker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. BMJ Ment Health. 2019;22(4):153–60.

    Google Scholar 

  22. Bürkner P-C. brms: an R package for Bayesian multilevel models using Stan. J Stat Softw. 2017;80:1–28.

    Article  Google Scholar 

  23. rjags: Bayesian graphical models using MCMC. R package version 4–16 [https://CRAN.R-project.org/package=rjags]

  24. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1–48.

    Article  Google Scholar 

  25. Garg R, Singh VK, Singh G. Comparison of haloperidol and quetiapine for treatment of delirium in critical illness: a prospective randomised double-blind placebo-controlled trial. J Clin Diagn Res. 2022. https://doiorg.publicaciones.saludcastillayleon.es/10.7860/JCDR/2022/56141.16615.

    Article  Google Scholar 

  26. Girard TD, Exline MC, Carson SS, Hough CL, Rock P, Gong MN, Douglas IS, Malhotra A, Owens RL, Feinstein DJ, et al. Haloperidol and ziprasidone for treatment of delirium in critical illness. N Engl J Med. 2018;379(26):2506–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Girard TD, Pandharipande PP, Carson SS, Schmidt GA, Wright PE, Canonico AE, Pun BT, Thompson JL, Shintani AK, Meltzer HY, et al. Feasibility, efficacy, and safety of antipsychotics for intensive care unit delirium: the MIND randomized, placebo-controlled trial. Crit Care Med. 2010;38(2):428–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. ORIC-I: optimizing recovery from intensive care: mechanical ventilation and delirium. [https://clinicaltrials.gov/study/NCT00300391]

  29. Page VJ, Ely EW, Gates S, Zhao XB, Alce T, Shintani A, Jackson J, Perkins GD, McAuley DF. Effect of intravenous haloperidol on the duration of delirium and coma in critically ill patients (Hope-ICU): a randomised, double-blind, placebo-controlled trial. Lancet Respir Med. 2013;1(7):515–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Smit L, Slooter AJC, Devlin JW, Trogrlic Z, Hunfeld NGM, Osse RJ, Ponssen HH, Brouwers A, Schoonderbeek JF, Simons KS, et al. Efficacy of haloperidol to decrease the burden of delirium in adult critically ill patients: the EuRIDICE randomized clinical trial. Crit Care. 2023;27(1):413.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Abdelgalel EF. Dexmedetomidine versus haloperidol for prevention of delirium during non-invasive mechanical ventilation. Egyp J Anaesth. 2016;32(4):473–81.

    Article  Google Scholar 

  32. Al-Qadheeb NS, Skrobik Y, Schumaker G, Pacheco MN, Roberts RJ, Ruthazer RR, Devlin JW. Preventing ICU subsyndromal delirium conversion to delirium with low-dose IV haloperidol: a double-blind. Placebo-Control Pilot Stud Crit Care Med. 2016;44(3):583–91.

    CAS  Google Scholar 

  33. Khan BA, Perkins AJ, Campbell NL, Gao S, Khan SH, Wang S, Fuchita M, Weber DJ, Zarzaur BL, Boustani MA, et al. Preventing postoperative delirium after major noncardiac thoracic surgery-a randomized clinical trial. J Am Geriatr Soc. 2018;66(12):2289–97.

    Article  PubMed  PubMed Central  Google Scholar 

  34. van den Boogaard M, Slooter AJC, Bruggemann RJM, Schoonhoven L, Beishuizen A, Vermeijden JW, Pretorius D, de Koning J, Simons KS, Dennesen PJW, et al. Effect of haloperidol on survival among critically Ill adults with a high risk of delirium: the reduce randomized clinical trial. JAMA. 2018;319(7):680–90.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wang W, Li HL, Wang DX, Zhu X, Li SL, Yao GQ, Chen KS, Gu XE, Zhu SN. Haloperidol prophylaxis decreases delirium incidence in elderly patients after noncardiac surgery: a randomized controlled trial*. Crit Care Med. 2012;40(3):731–9.

    Article  PubMed  Google Scholar 

  36. Moots RJ, Al-Saffar Z, Hutchinson D, Golding SP, Young SP, Bacon PA, McLaughlin PJ. Old drug, new tricks: haloperidol inhibits secretion of proinflammatory cytokines. Ann Rheum Dis. 1999;58(9):585–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Song C, Lin A, Kenis G, Bosmans E, Maes M. Immunosuppressive effects of clozapine and haloperidol: enhanced production of the interleukin-1 receptor antagonist. Schizophr Res. 2000;42(2):157–64.

    Article  CAS  PubMed  Google Scholar 

  38. Tulgar S, Ahiskalioglu A, Kok A, Thomas DT. Possible old drugs for repositioning in COVID-19 treatment: combating cytokine storms from haloperidol to anti-interleukin agents. Turk J Anaesthesiol Reanim. 2020;48(3):256–7.

    Article  CAS  PubMed  Google Scholar 

  39. Andrews PS, Wang S, Perkins AJ, Gao S, Khan S, Lindroth H, Boustani M, Khan B. Relationship between intensive care unit delirium severity and 2-year mortality and health care utilization. Am J Crit Care. 2020;29(4):311–7.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Shehabi Y, Riker RR, Bokesch PM, Wisemandle W, Shintani A, Ely EW, Group SS. Delirium duration and mortality in lightly sedated, mechanically ventilated intensive care patients. Crit Care Med. 2010;38(12):2311–8.

    Article  PubMed  Google Scholar 

  41. Marra A, Vargas M, Buonanno P, Iacovazzo C, Kotfis K, Servillo G. Haloperidol for preventing delirium in ICU patients: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci. 2021;25(3):1582–91.

    CAS  PubMed  Google Scholar 

  42. Beach SR, Gross AF, Hartney KE, Taylor JB, Rundell JR. Intravenous haloperidol: a systematic review of side effects and recommendations for clinical use. Gen Hosp Psychiatry. 2020;67:42–50.

    Article  PubMed  Google Scholar 

Download references

Funding

This work is supported by MacKay Medical College in Taiwan (MMC-RD-111-1B-P003, for SLC) and the National Science and Technology Council in Taiwan (NSTC 112–2314-B-715–003, for SLC). The funding source had no role in any process of our study.

Author information

Author notes

  1. Shu-Li Cheng and Tien-Wei Hsu contributed equally to this article as first authors.

Authors and Affiliations

  1. Department of Nursing, Mackay Medical College, Taipei, Taiwan

    Shu-Li Cheng

  2. Department of Psychiatry, E-DA Dachang Hospital, I-Shou University, Kaohsiung, Taiwan

    Tien-Wei Hsu

  3. Department of Psychiatry, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan

    Tien-Wei Hsu

  4. Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan

    Tien-Wei Hsu

  5. School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan

    Tien-Wei Hsu

  6. Department of Psychiatry, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan

    Yu-Chen Kao & Chih-Sung Liang

  7. Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, No. 60, Xinmin Road, Beitou District, Taipei, 112, Taiwan

    Yu-Chen Kao & Chih-Sung Liang

  8. Department of Pharmacy, Chang Gung Memorial Hospital Linkou, Taipei, Taiwan

    Chia-Ling Yu

  9. Centre for Chronic Illness and Ageing, University of Greenwich, London, UK

    Trevor Thompson

  10. IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health, Deakin University, Geelong, VIC, Australia

    Andre F. Carvalho

  11. Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Brendon Stubbs

  12. Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Ping-Tao Tseng

  13. Department of Psychology, College of Medical and Health Science, Asia University, Taichung, Taiwan

    Ping-Tao Tseng

  14. Prospect Clinic for Otorhinolaryngology & Neurology, Kaohsiung, Taiwan

    Ping-Tao Tseng

  15. Institute of Precision Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan

    Ping-Tao Tseng

  16. Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan

    Chih-Wei Hsu

  17. Department of Neurology, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan

    Fu-Chi Yang

  18. Institute of Epidemiology & Preventive Medicine, College of Public Health, National Taiwan University, No. 17, Xuzhou Road, Zhongzheng District, Taipei, 100, Taiwan

    Yu-Kang Tu

  19. Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan

    Yu-Kang Tu

Authors
  1. Shu-Li Cheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Tien-Wei Hsu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yu-Chen Kao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Chia-Ling Yu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Trevor Thompson
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Andre F. Carvalho
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Brendon Stubbs
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Ping-Tao Tseng
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Chih-Wei Hsu
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Fu-Chi Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Yu-Kang Tu
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Chih-Sung Liang
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Tien-Wei, Hsu and Shu-Li Cheng contributed equally to this work as first authors. Yu-Kang Tu and Chih-Sung Liang contributed equally to this work and are joint last/corresponding authors. Chih-Sung Liang, Tien-Wei Hsu, and Yu-Kang Tu conceived and designed the study. Tien-Wei Hsu, Shu-Li Cheng, Chia-Ling Yu, Chih-Wei Hsu, and Ping-Tao Tseng selected the articles, extracted the data, and assess the risk of bias. Chia-Ling Yu did the systemic search. Tien-Wei Hsu and Chih-Sung Liang wrote the first draught of the manuscript. Jia-Ru, Li, Trevor Thompson, Andre F. Carvalho, Brendon Stubbs, Fu-Chi Yang, Yu-Kang Tu interpreted the data and contributed to the writing of the final version of the manuscript. Chih-Sung Liang and Tien-Wei Hsu have accessed and verified the data. Chih-Sung Liang and Yu-Kang Tu were responsible for the decision to submit the manuscript. All authors confirmed that they had full access to all the data in the study and accept responsibility to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Corresponding authors

Correspondence to Yu-Kang Tu or Chih-Sung Liang.

Ethics declarations

Ethics approval

Not required; analysis of aggregated identified clinical trial data.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, SL., Hsu, TW., Kao, YC. et al. Haloperidol in treating delirium, reducing mortality, and preventing delirium occurrence: Bayesian and frequentist meta-analyses. Crit Care 29, 126 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13054-025-05342-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13054-025-05342-6

Keywords

Critical Care

ISSN: 1364-8535