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A Competing-Risk Approach for Modeling Length of Stay in Severe Malaria Patients in South-East Asia and the Implications for Planning of Hospital Services.

ABSTRACT: Background:Management of severe malaria with limited resources requires comprehensive planning. Expected length of stay (LOS) and the factors influencing it are useful in the planning and optimisation of service delivery. Methods:A secondary, competing-risk approach to survival analysis was performed for 1217 adult severe malaria patients from the South-East Asia Quinine Artesunate Malaria Trial. Results:Twenty percent of patients died; 95.4% within 7 days compared to 70.3% of those who were discharged. Median time to discharge was 6 days. Compared to quinine, artesunate increased discharge incidence (subdistribution-Hazard ratio, 1.24; [95% confidence interval 1.09-1.40]; P = .001) and decreased incidence of death (0.60; [0.46-0.80]; P < .001). Low Glasgow coma scale (discharge, 1.08 [1.06-1.11], P < .001; death, 0.85 [0.82-0.89], P < .001), high blood urea-nitrogen (discharge, 0.99 [0.99-0.995], P < .001; death, 1.00 [1.00-1.01], P = .012), acidotic base-excess (discharge, 1.05 [1.03-1.06], P < .001; death, 0.90 [0.88-0.93], P < .001), and development of shock (discharge, 0.25 [0.13-0.47], P < .001; death, 2.14 [1.46-3.12], P < .001), or coma (discharge, 0.46 [0.32-0.65], P < .001; death, 2.30 [1.58-3.36], P < .001) decreased cumulative incidence of discharge and increased incidence of death. Conventional Kaplan-Meier survival analysis overestimated cumulative incidence compared to competing-risk model. Conclusions:Clinical factors on admission and during hospitalisation influence LOS in severe malaria, presenting targets to improve health and service efficiency. Artesunate has the potential to increase LOS, which should be accounted for when planning services. In-hospital death is a competing risk for discharge; an important consideration in LOS models to reduce overestimation of risk and misrepresentation of associations.

SUBMITTER: Keene CM

PROVIDER: S-EPMC6137114 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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A Competing-Risk Approach for Modeling Length of Stay in Severe Malaria Patients in South-East Asia and the Implications for Planning of Hospital Services.

Keene Claire M CM Dondorp Arjen A Crawley Jane J Ohuma Eric O EO Mukaka Mavuto M

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20180901 7

<h4>Background</h4>Management of severe malaria with limited resources requires comprehensive planning. Expected length of stay (LOS) and the factors influencing it are useful in the planning and optimisation of service delivery.<h4>Methods</h4>A secondary, competing-risk approach to survival analysis was performed for 1217 adult severe malaria patients from the South-East Asia Quinine Artesunate Malaria Trial.<h4>Results</h4>Twenty percent of patients died; 95.4% within 7 days compared to 70.3% ...[more]

PMID: 29562258

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Project description:Current outcome predictors for illness and injury are measured at a single time point-admission. However, patient prognosis often changes during hospitalization, limiting the usefulness of those predictions. Accurate depiction of the dynamic interaction between competing events during hospitalization may enable real-time outcome assessment.To determine how the effects of burn outcome predictors (ie, age, total body surface area burn, and inhalation injury) and the outcomes of interest (ie, mortality and length of stay) vary as a function of time throughout hospitalization.In this retrospective study, we used the American Burn Association's National Burn Repository, containing outcomes and patient and injury characteristics, to identify 95?579 patients admitted with an acute burn injury to 80 tertiary American Burn Association burn centers from 2000 through 2009. We applied competing risk statistical methods to analyze patient outcomes.We estimated the cause-specific hazard rates for death and discharge to assess how the instantaneous risk of these events changed across time. We further evaluated the varying effects of patient age, total body surface area burn, and inhalation injury on the probability of discharge and death across time.Maximum length of stay among patients who died was 270 days and 731 days among those discharged. Total body surface area, age, and inhalation injury had significant effects on the subdistribution hazard for discharge (P?<?.001); these effects varied across time (P?<?.002). Burn size (coefficient -0.046) determined early outcomes, while age (coefficient -0.034) determined outcomes later in the hospitalization. Inhalation injury (coefficient -0.622) played a variable role in survival and hospital length of stay.Real-time measurement of dynamic interrelationships among burn outcome predictors using competing risk analysis demonstrated that the key factors influencing outcomes differed throughout hospitalization. Further application of this analytic technique to other injury or illness types may improve assessment of outcomes.

Project description:BackgroundPredicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data.MethodOn a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy.ResultsAll methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days.ConclusionsGiven the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Project description:IntroductionLength of hospital stay (LOS), defined as the time from inpatient admission to discharge, death, referral, or abscondment, is one of the key indicators of quality in patient care. Reduced LOS lowers health care expenditure and minimizes the chance of in-hospital acquired infections. Conventional methods for estimating LOS such as the Kaplan-Meier survival curve and the Cox proportional hazards regression for time to discharge cannot account for competing risks such as death, referral, and abscondment. This study applied competing risk methods to investigate factors important for risk-stratifying patients based on LOS in order to enhance patient care.MethodsThis study analyzed data from ongoing safety surveillance of the malaria vaccine implementation program in Malawi's four district hospitals of Balaka, Machinga, Mchinji, and Ntchisi. Children aged 1-59 months who were hospitalized (spending at least one night in hospital) with a medical illness were consecutively enrolled between 1 November 2019 and 31 July 2021. Sub-distribution-hazard (SDH) ratios for the cumulative incidence of discharge were estimated using the Fine-Gray competing risk model.ResultsAmong the 15,463 children hospitalized, 8,607 (55.7%) were male and 6,856 (44.3%) were female. The median age was 22 months [interquartile range (IQR): 12-33 months]. The cumulative incidence of discharge was 40% lower among HIV-positive children compared to HIV-negative (sub-distribution-hazard ratio [SDHR]: 0.60; [95% CI: 0.46-0.76]; P < 0.001); lower among children with severe and cerebral malaria [SDHR: 0.94; (95% CI: 0.86-0.97); P = 0.04], sepsis or septicemia [SDHR: 0.90; (95% CI: 0.82-0.98); P = 0.027], severe anemia related to malaria [SDHR: 0.54; (95% CI: 0.48-0.61); P < 0.001], and meningitis [SDHR: 0.18; (95% CI: 0.09-0.37); P < 0.001] when compared to non-severe malaria; and also 39% lower among malnourished children compared to those that were well-nourished [SDHR: 0.61; (95% CI: 0.55-0.68); P < 0.001].ConclusionsThis study applied the Fine-Gray competing risk approach to more accurately model LOS as the time to discharge when there were significant rates of in-hospital mortality, referrals, and abscondment. Patient care can be enhanced by risk-stratifying by LOS based on children's age, HIV status, diagnosis, and nutritional status.

Project description:IntroductionCivic engagement (CE) has the potential to transform mental health services and could be particularly important for low and middle-income countries (LMICs), which are rapidly developing to respond to the burden of poor mental health. Research from high income countries has found many challenges associated with the meaningful implementation of CE in practice, but this has been underexplored in LIMCS and in South East Asia (SEA) in particular.MethodsWe completed a realist synthesis and systematic review of peer reviewed publications and grey literature to identify the context and actions which promote successful implementation of CE approaches in SEA. We used a theory-driven approach-realist synthesis-to analyse data and develop context-mechanism-outcome configurations that can be used to explain how civic engagement approaches operate in South East Asian contexts. We worked closely with patient and public representatives to guide the review from the outset.ResultsFifty-seven published and unpublished articles were included, 24 were evaluations of CE, including two Randomized Controlled Trials. The majority of CE interventions featured uptake or adaptation of Western models of care. We identified important cultural differences in the enactment of civic engagement in SEA contexts and four mechanisms which, alongside their contextual barriers and facilitators, can be used to explain how civic engagement produces a range of outcomes for people experiencing mental health problems, their families and communities. Our review illustrates how CE interventions can be successfully implemented in SEA, however Western models should be adapted to fit with local cultures and values to promote successful implementation. Barriers to implementation included distrust of services/outside agencies, stigma, paternalistic cultures, limited resource and infrastructure.ConclusionOur findings provide guidance for the implementation of CE approaches within SEA contexts and identify areas for further research. Due to the collectivist nature of many SEA cultures, and the impact of shared traumas on community mental health, CE might best be implemented at community level, with a focus on relational decision making.Registration This review is registered on PROSPERO: CRD42018087841.

Dataset Information

A Competing-Risk Approach for Modeling Length of Stay in Severe Malaria Patients in South-East Asia and the Implications for Planning of Hospital Services.

Publications

A Competing-Risk Approach for Modeling Length of Stay in Severe Malaria Patients in South-East Asia and the Implications for Planning of Hospital Services.

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