Optimización perioperatoria del volumen de líquidos después de una fractura proximal de fémur
Resumen
Antecedentes
La fractura proximal de fémur es una urgencia ortopédica frecuente que afecta principalmente a personas de edad avanzada con alto riesgo de complicaciones. Se dispone de métodos avanzados para controlar el tratamiento de los líquidos durante el tratamiento de la fractura proximal de fémur, pero su función en la reducción del riesgo no está clara.
Objetivos
Comparar la seguridad y la eficacia de los siguientes métodos de optimización perioperatoria de los líquidos en participantes adultos sometidos a reparación quirúrgica de una fractura de cadera: monitorización hemodinámica invasiva avanzada, como Doppler transesofágico y análisis del contorno del pulso; un protocolo que utiliza medidas estándar, como la presión arterial, la diuresis y la presión venosa central; y la atención habitual.
En otras revisiones se consideran comparaciones de tipos de líquidos (p.ej., cristaloide versus coloides) y otros métodos para optimizar la administración de oxígeno, como los tratamientos con productos sanguíneos y el tratamiento farmacológico con fármacos inotrópicos y vasoactivos.
Métodos de búsqueda
Se hicieron búsquedas en el Registro Cochrane Central de Ensayos Controlados (Cochrane Central Register of Controlled Trials) (CENTRAL; 2015, número 9); MEDLINE (octubre de 2012 hasta septiembre de 2015); y EMBASE (octubre de 2012 hasta septiembre de 2015) sin restricciones de idioma. Se realizaron búsquedas de citas prospectivas y retrospectivas en los ensayos identificados. Se realizaron búsquedas en ClinicalTrials.gov y en la International Clinical Trials Registry Platform de la Organización Mundial de la Salud (OMS) para encontrar ensayos no publicados. Esta es una versión actualizada de una revisión publicada originalmente en 2004 y actualizada primero en 2013 y nuevamente en 2015. Las búsquedas originales se realizaron en octubre de 2003 y octubre de 2012.
Criterios de selección
Se incluyeron los ensayos controlados aleatorizados (ECA) en participantes adultos sometidos a tratamiento quirúrgico de una fractura proximal de fémur que compararon dos de cualquiera entre la monitorización hemodinámica avanzada, los protocolos que utilizan medidas estándar o la atención habitual, independientemente del cegamiento, el idioma o el estado de publicación.
Obtención y análisis de los datos
Dos autores de la revisión evaluaron la repercusión de las intervenciones de optimización de los líquidos sobre los resultados de mortalidad, duración de la estancia hospitalaria, tiempo transcurrido hasta que se consideraron aptos desde el punto de vista médico para el alta, si los participantes pudieron regresar a la residencia previa a la fractura a los seis meses, la movilidad de los participantes a los seis meses y los eventos adversos en el hospital. Los datos se agruparon mediante el riesgo relativo (RR) o la diferencia de medias (DM) para los datos dicotómicos o continuos, respectivamente, sobre la base de modelos de efectos aleatorios.
Resultados principales
En esta revisión actualizada se incluyeron cinco ECA con un total de 403 participantes, y se agregaron dos nuevos ensayos identificados durante la búsqueda de 2015. Se determinó que uno de los estudios incluidos tuvo alto riesgo de sesgo; ningún ensayo informó sobre todos los resultados predeterminados. Se encontraron dos ensayos de los que se esperan datos para su clasificación y un ensayo en curso.
Tres estudios compararon la monitorización hemodinámica avanzada con un protocolo que utilizó medidas estándar; tres compararon la monitorización hemodinámica avanzada con la atención habitual; y uno comparó un protocolo que utilizó medidas estándar con la atención habitual. Los metanálisis de las dos comparaciones de la monitorización hemodinámica avanzada son consistentes con un aumento y una disminución del riesgo de mortalidad (RR de efectos aleatorios de Mantel‐Haenszel [M‐H] 0,41; intervalo de confianza [IC] del 95%: 0,14 a 1,20; 280 participantes; RR de efectos aleatorios de M‐H 0,45; IC del 95%: 0,07 a 2,95; 213 participantes, respectivamente). El estudio que comparó un protocolo con la atención habitual no encontró diferencias entre los grupos en este resultado.
Tres estudios que compararon la monitorización hemodinámica avanzada con la atención habitual informaron datos sobre la duración de la estancia y el tiempo transcurrido hasta que se consideraron aptos desde el punto de vista médico para el alta. No hubo diferencias estadísticamente significativas entre los grupos en estos resultados en los dos estudios que se pudieron combinar (DM VI efectos fijos 0,63; IC del 95%: ‐1,70 a 2,96; DM VI efectos fijos 0,01; IC del 95%: ‐1,74 a 1,71, respectivamente) y no hubo diferencias estadísticamente significativas en el tercer estudio. En un estudio se informó una reducción del tiempo transcurrido hasta que se consideraron aptos desde el punto de vista médico para el alta al comparar la monitorización hemodinámica avanzada con un protocolo, y al comparar el protocolo de monitorización con la atención habitual.
El número de participantes con una o más complicaciones no mostró diferencias estadísticamente significativas en cada una de las dos comparaciones de monitorización hemodinámica avanzada (RR M‐H de efectos aleatorios 0,83; IC del 95%: 0,59 a 1,17; 280 participantes; RR M‐H de efectos aleatorios 0,72; IC del 95%: 0,40 a 1,31; 173 participantes, respectivamente), ni tampoco diferencias en la comparación del protocolo y la atención habitual.
Sólo un estudio informó sobre el número de participantes capaces de volver a su residencia habitual después del alta, sin diferencias estadísticamente significativas entre los grupos.
Hubo pocos estudios con un número reducido de participantes, y al utilizar el enfoque GRADE (Grades of Recommendation, Assessment, Development and Evaluation Working Group), se consideró que la calidad de la evidencia de los resultados fue baja. Se incluyó un estudio con un alto riesgo de sesgo, pero al aplicar GRADE, la calidad de esta evidencia de resultados se disminuyó a muy baja.
Conclusiones de los autores
Cinco estudios, que incluyeron un total de 403 participantes, no proporcionaron evidencia de que las estrategias de optimización de los líquidos mejoren los resultados de los participantes que se someten a una cirugía por fractura proximal de fémur. Se realizan más estudios de investigación para probar algunos de estos resultados.
PICO
Resumen en términos sencillos
Optimización de los niveles de líquidos en pacientes con fracturas de cadera
Antecedentes
Las fracturas de cadera son frecuentes entre las personas de edad avanzada, que a menudo presentan afecciones médicas que los ponen en riesgo de desarrollar otros problemas mientras se trata su fractura. El tratamiento suele consistir en una cirugía para reparar la fractura del hueso y si se le proporciona mucho o muy poco líquido al paciente en ese momento, puede aumentar el riesgo de problemas adicionales. El personal sanitario utiliza muchos enfoques para determinar la cantidad de líquido que necesita un paciente en esta situación, pero no está claro si algunos métodos son mejores que otros. En esta revisión Cochrane se analizaron los estudios de investigación sobre los efectos de los diferentes métodos para encontrar los niveles máximos de líquidos efectivos para los hombres y mujeres adultos que se someten a una cirugía por cualquier tipo de fractura de cadera.
Características de los estudios
La evidencia está actualizada hasta septiembre de 2015. Se encontraron cinco estudios con 403 participantes, cada uno de los cuales comparó dos o tres métodos de guiar el tratamiento de los líquidos. Estos métodos incluyen la "atención habitual" (en la que el personal utiliza cambios en las mediciones básicas, como la frecuencia cardíaca, para decidir cuánto líquido se debe administrar), los "protocolos que utilizan medidas estándar" (en los que el personal utiliza cambios en las mediciones básicas cuando administra los líquidos según un conjunto de normas formales) y la "monitorización hemodinámica avanzada" (en la que el personal utiliza equipo invasivo, como dispositivos especializados de motorización de la presión sanguínea colocados en las arterias, para determinar cuánto líquido se debe administrar).
Resultados clave
En estos ensayos no se encontró evidencia que indique que el uso de un método en lugar de otro reduce los efectos perjudiciales, incluida la muerte, o disminuye el número de complicaciones. Cuando se combinaron los resultados de los estudios no se encontró evidencia que indicara que algún método redujera la duración de la estancia hospitalaria ni el tiempo en que los participantes se consideraron aptos desde el punto de vista médico para el alta. Los resultados tampoco mostraron una diferencia en el número de participantes capaces de volver a su residencia normal después del alta.
Calidad de la evidencia
Se encontraron pocos estudios relevantes con un número reducido de participantes. La diferencia de tiempo entre el estudio más antiguo, publicado en 1985, y el último estudio, publicado en 2014, indica que la práctica estándar para el tratamiento de la fractura de cadera puede diferir entre estos estudios. Un estudio se consideró con alto riesgo de sesgo y se utilizó el enfoque GRADE para evaluar la calidad de la evidencia como baja o muy baja. Los resultados de la revisión son aplicables únicamente a los países en los que se realizaron los estudios pertinentes, ya que la "atención habitual" puede diferir en otros países.
Conclusión
La evidencia actual no es suficiente para mostrar qué método de detección de los niveles máximos efectivos de líquidos en los pacientes sometidos a una cirugía de fractura de cadera es preferible.
Authors' conclusions
Summary of findings
| Advanced haemodynamic monitoring compared with protocol using standard measures such as CVP for proximal femoral fracture | ||||||
| Patient or population: patients with proximal femoral fracture | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Protocol using standard measures such as CVP | Advanced haemodynamic monitoring | |||||
| All‐cause mortality | Study population | RR 0.41 | 280 | ⊕⊝⊝⊝ | ||
| 142 per 1000 | 58 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Not estimabled | 203 | ⊕⊕⊝⊝ | Data reported as median (range) in Bartha 2013 and as mean (95% confidence interval) in Venn 2002 | ||
| Medically fit for discharge | Mean medically fit for discharge in the intervention groups was | 90 | ⊕⊕⊝⊝ | |||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Not estimable | ‐ | Not reported | |||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | |||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Not estimable | 0 | ||||
| Adverse outcomes ‐ all | Study population | RR 0.90 | 280 | ⊕⊝⊝⊝ | ||
| 319 per 1000 | 287 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aConcerns about randomization process in Schultz 1985; high risk of selection bias eData from 1 study only. Downgraded 2 levels | ||||||
| Advanced haemodynamic monitoring compared with usual care for perioperative fluid optimization | ||||||
| Patient or population: patients with perioperative fluid optimization | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Usual care | Advanced haemodynamic monitoring | |||||
| All‐cause mortality | Study population | RR 0.45 | 213 | ⊕⊕⊝⊝ | ||
| 89 per 1000 | 40 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Mean total length of hospital stay in the control groups was | Mean total length of hospital stay in the intervention groups was | 175 | ⊕⊕⊝⊝ | ||
| Medically fit for discharge | Mean medically fit for discharge in the control groups was | Mean medically fit for discharge in the intervention groups was | 175 | ⊕⊕⊝⊝ | ||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Study population | Not estimable | 114 | ⊕⊕⊝⊝ | ||
| 397 per 1000 | 0 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | |||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Study population | RR 1.10 | 173 | ⊕⊕⊝⊝ | ||
| 152 per 1000 | 170 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ all | Study population | RR 0.78 | 173 | ⊕⊕⊝⊝ | ||
| 554 per 1000 | 432 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aConfidence intervals cross no effect and are consistent with increased as well as decreased risk. Downgraded 1 level | ||||||
| Protocol using standard measures such as CVP compared with usual care for perioperative fluid optimization | ||||||
| Patient or population: patients with perioperative fluid optimization | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Usual care | Protocol using standard measures such as CVP | |||||
| All‐cause mortality | Study population | RR 2.81 | 60 | ⊕⊕⊝⊝ | ||
| 69 per 1000 | 194 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Mean total length of hospital stay in the control groups was | Mean total length of hospital stay in the intervention groups was | 60 | ⊕⊕⊝⊝ | ||
| Medically fit for discharge | Mean medically fit for discharge in the intervention groups was | 60 | ⊕⊕⊕⊝ | |||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Not estimable | ‐ | Not reported | |||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | Not reported | ||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Study population | RR 0.94 | 60 | ⊕⊕⊝⊝ | ||
| 34 per 1000 | 32 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ all | Study population | RR 0.53 | 60 | ⊕⊕⊝⊝ | ||
| 483 per 1000 | 256 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aBased on 1 study with a small number of events. Wide confidence intervals consistent with increased as well as decreased risk. Downgraded by 1 level | ||||||
Background
Description of the condition
Proximal femoral fractures (PFFs), or hip fractures, are fractures of the femur immediately distal to the articular surface of the hip joint to about 5 cm below the lesser trochanter. They can be subdivided into intracapsular and extracapsular fractures. Intracapsular (also termed transcervical or subcapital) fractures occur proximal to the trochanteric line, and extracapsular (also termed pertrochanteric, subtrochanteric, trochanteric or intertrochanteric) fractures occur distal to the trochanteric line, up to 5 cm below the lower border of the lesser trochanter.
These fractures occur most commonly in elderly people with osteoporosis after a simple mechanical fall. Approximately 1.5 million hip fractures are reported per year worldwide, and the number of hip fractures is projected to increase to 4.5 million by the year 2050 (Gullberg 1997; Sterling 2011). Incidence varies by country, from about 50 to 500 per 100,000, and is about two times higher among females than males, although this difference varies with race (Kanis 2012; Kannus 1996).
PFF is one of the most common orthopaedic emergencies, and most cases are managed by early surgical fixation to reduce complications from prolonged immobility associated with conservative treatment. Limited evidence has been obtained from randomized controlled trials (RCTs) to inform this practice, but other types of studies have shown increased risk of death when surgery is delayed; it would be difficult to conduct ethically sound trials that compare operative with conservative treatment (Bottle 2006; Handoll 2008). Generally, medically fit patients should undergo surgery within 24 hours.
Undisplaced intracapsular fractures usually are treated by internal fixation to preserve the femoral head, with screws or pins, with or without plates, fixed to the femur. Displaced intracapsular fractures may be reduced and internally fixated or may undergo replacement arthroplasty. Extracapsular fractures may be fixed with a screw passed up the femoral neck to the head,then attached to a plate on the side of the femur, or an intramedullary nail may be used with a side screw passed up into the femoral head.
Description of the intervention
Age‐related co‐morbidities and dehydration in people presenting with PFF put them at increased risk for peritraumatic and perioperative complications. Providing adequate fluid resuscitation is important in minimizing this risk. The adequacy of fluid therapy may be determined by simple, readily available clinical measures, such as tissue turgor, heart rate, blood pressure, urine output and central venous pressure (CVP). However, these are non‐specific and poorly sensitive measures of fluid optimization (Marik 2008). Growing evidence suggests that predicting responsiveness to fluid therapy is more important (Funk 2009). The aim is to use goal‐directed fluid therapy to optimize cardiac output, to avoid overloading the cardiovascular system and precipitating heart failure. This approach, along with adequate haemoglobin and inspired oxygen levels, optimizes delivery of oxygen to tissues and organs and may improve outcomes (Green 2010).
One way to assess fluid responsiveness is to use a protocol that combines several simple measures to determine the effect of a standardized fluid bolus and to decide whether additional fluid will provide benefit for the patient. Another method is to use advanced haemodynamic monitoring techniques to detect cardiovascular changes that occur with incremental fluid boluses, to predict responsiveness to increased fluid. Although some of these advanced techniques are in their infancy, several have become established in clinical practice. These approaches can be split into static measures of cardiac preload (load placed on the heart by blood returning to it) and dynamic measures of interactions between heart and lung.
Static measures aim to determine cardiac preload but fail to estimate response to fluids in about one‐half of patients, thus rendering them exposed to the hazards of unnecessary fluid therapy (Eyre 2010). Despite this, many of these measures are in clinical use. Right ventricular end‐diastolic volume can be measured by a fast‐response thermistor pulmonary artery catheter or by cardiac scintigraphy. Transoesophageal echocardiography (TOE) can measure left ventricular (LV) end‐diastolic area, which correlates well with left ventricular end‐diastolic volume ‐ a measure of LV preload. Transpulmonary thermodilution measured by a commercially available device (PiCCO; Pulsion Medical Systems, Feldkirchen, Germany) assesses global end‐diastolic volume (GEDV), the largest volume of blood contained within the four heart chambers, and intrathoracic blood volume; both measurements have been validated as indicators of cardiac preload (Bendjelid 2010; Muller 2008). Pulmonary artery (Swan‐Ganz) catheters are inserted into the pulmonary artery to measure pulmonary artery occlusion pressure (PAOP); however, their use has been reduced over recent years, as PAOP has been shown to be a poor marker of left ventricular end‐diastolic volume, and therefore of cardiac preload and cardiac output (Maus 2008).
Dynamic measures are generally superior to static measures in predicting fluid responsiveness, although this has been demonstrated mainly in sedated ventilated patients (Eyre 2010). Various technologies use these measures, which include stroke volume variation (SVV), systolic pressure variation (SPV), pulse pressure variation (PPV), aortic blood velocity (ABV), superior vena cava collapsibility index (SVCCI) and inferior vena cava distensibility index (IVCDI). The commercially available LiDCO device (Vigileo/FloTrac, Irvine, California, USA) analyses the waveform of the arterial blood pressure pulse for SVV, SPV and PPV; transthoracic echocardiography (TTE) measures IVCDI; oesophageal Doppler measures SVV and ABV; TOE can measure SVCCI and ABV; and PiCCO can measure SVV. LiDCO and TTE can be used with patients who are awake when undergoing regional anaesthesia, and with those who are unconscious when undergoing general anaesthesia. Oesophageal Doppler and TOE can be used only with patients who are undergoing general anaesthesia.
How the intervention might work
Major surgery and critical illness are associated with increased oxygen demand due to a systemic inflammatory response, the stress response and increased metabolic activity. Inadequate fluid resuscitation and cardiopulmonary disease may reduce the supply of adequate tissue blood flow and delivery of oxygen. This may result in cellular dysfunction, organ damage, organ failure and ultimately death. Fluid overload is also harmful, potentially causing cardiac performance to fall as the result of extreme right shift on the Starling myocardial performance curve, respiratory failure due to fluid accumulation in the lungs, gastric dysmotility and poor wound healing. Growing evidence indicates that standardized methods to optimize fluid and oxygen delivery to tissues may decrease morbidity and mortality in a variety of clinical settings, particularly among high‐risk surgical patients and those with critical illness or sepsis (Lees 2009).
Why it is important to do this review
Protocols, or advanced methods of managing fluid therapy, may improve various outcomes in the large number of people who suffer from PFF each year. However, these methods may cause harm and may incur financial cost. A systematic evaluation of the current evidence is needed to assist clinicians who attempt to optimize fluid volume status in people undergoing surgery for PFF. We selected our included outcomes according to their frequency in studies of PFF and their usefulness in clinical decision making (Liem 2012).
This is the second update of a Cochrane review originally published in 2004 (Price 2004) and first updated in 2013 (Brammar 2013). New monitoring techniques and revised methods have been introduced within Cochrane; therefore, we have re‐run the searches, including extra search terms, and have used different methods to assess study quality.
Objectives
To compare the safety and effectiveness of the following methods of perioperative fluid optimization in adult participants undergoing surgical repair of hip fracture: advanced invasive haemodynamic monitoring, such as transoesophageal Doppler and pulse contour analysis; a protocol using standard measures, such as blood pressure, urine output and central venous pressure; and usual care.
Comparisons of fluid types (e.g. crystalloid vs colloid) and other methods of optimizing oxygen delivery, such as blood product therapies and pharmacological treatment with inotropes and vasoactive drugs, are considered in other reviews.
Methods
Criteria for considering studies for this review
Types of studies
We included only randomized controlled trials (RCTs). If we could have obtained adequate method and results data, we would have considered for inclusion cluster‐randomized trials, quasi‐randomized trials (e.g. alternation) and trials in which treatment allocation was inadequately concealed, as well as unpublished studies and studies published only in abstract form. We did not expect to identify any cross‐over trials for this condition.
Types of participants
We included studies on adults who underwent acute surgical treatment of any type for PFF while under regional or general anaesthesia.
Types of interventions
We included studies that compared use of any two of the following.
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Advanced invasive haemodynamic monitoring, such as transoesophageal Doppler and pulse contour analysis.
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A protocol using standard measures, such as blood pressure, urine output and central venous pressure.
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Usual care.
We undertook reviews of three different comparisons.
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Advanced haemodynamic monitoring versus a protocol using standard measures.
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Advanced haemodynamic monitoring versus usual care.
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A protocol using standard measures versus usual care.
Types of outcome measures
Primary outcomes
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All‐cause mortality (within 30 days if reported, otherwise as reported in the trial).
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Length of hospital stay.
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Total length of hospital stay.
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Time to medical fitness for discharge.
-
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Return of participant to pre‐fracture category of accommodation at six months.
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Return to pre‐fracture mobility at six months.
Secondary outcomes
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Major adverse events in hospital.
-
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Iatrogenic (related to intervention, e.g. pneumothorax, haemothorax, upper limb thrombosis, line sepsis, local haematoma).
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Cardiopulmonary (e.g. myocardial infarction, cardiac or respiratory failure, thromboembolic event).
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Neurological (e.g. delirium, postoperative cognitive dysfunction, cerebrovascular accident).
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We also recorded any complications reported in the study, including minor events.
Outcomes did not form part of the study eligibility assessment. We included in the review studies that met design, participant and intervention criteria, even if they did not report relevant outcomes.
Search methods for identification of studies
Electronic searches
We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2015, Issue 9; see Appendix 1); MEDLINE via Ovid SP (October 2012 to September 2015; see Appendix 2); and EMBASE via Ovid SP (October 2012 to September 2015; see Appendix 3) for relevant randomized trials published in any language. When searching MEDLINE, we combined our topic‐specific keywords with the Cochrane highly sensitive search strategy for identifying RCTs (Higgins 2011). We modified this filter for use in EMBASE and used specific keywords to identify potential studies (see Appendix 1; Appendix 2; Appendix 3).
We performed original searches for the review in October 2003 (Price 2004) and October 2012 (Brammar 2013).
We searched for ongoing clinical trials and unpublished studies on the following Internet sites (July 2015).
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ClinicalTrials.gov trials registry (see Appendix 4).
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World Health Organization International Clinical Trials Registry Platform (see Appendix 5).
Searching other resources
We undertook backward and forward citation searching for key review articles identified through the initial searches (see Appendix 6). We used Google Scholar for forward citation searching. We read the reference lists of included articles for backward citations.
We contacted investigators to ask for details of ongoing studies and to request unpublished data needed for our analyses.
Data collection and analysis
Selection of studies
Two review authors, Sharon R. Lewis (SRL) and Andrew R. Butler (ARB), independently screened all titles and abstracts identified by the searches for potentially eligible trials. We removed studies that were very unlikely to be eligible. If no abstract was available but the title was possibly relevant, we obtained the full text of the article. The same review authors then independently examined the full text of all remaining articles and made a joint decision at that time regarding inclusion.
Data extraction and management
Review authors SRL and ARB independently extracted and collected data using Covidence software (Covidence) based on the previously used data extraction form (see Appendix 7). No blinding of the study author, the institution or the publication source of the studies was performed. We resolved disagreements by discussion and consensus, and finally with involvement of a third review author (AFS).
Assessment of risk of bias in included studies
Review authors independently assessed risk of bias using the Cochrane 'Risk of bias' tool (Higgins 2011a). We assessed the following six domains: sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data and selective outcome reporting.
We considered blinding and incomplete outcome data separately for each outcome. Blinding of participants was of particular importance for patient‐reported outcomes such as mobility. Blinding of assessors was particularly important for outcomes such as cognitive function that may be prone to detection bias.
Measures of treatment effect
For dichotomous outcomes (e.g. mortality, adverse outcomes), we entered numbers of events and total number within each randomization group into RevMan 5.3 (RevMan 5.3) and calculated risk ratios (RRs) with 95% confidence intervals (CIs) to express effect size. If data were presented in other forms, such as hazard or odds ratios, and if we were unable to obtain the required tabular data from study authors, we planned to enter these and to use the generic inverse variance option in RevMan 5.3. For continuous measures, such as length of stay, we calculated weighted mean differences when means and standard deviations were available. We calculated standard deviations from 95% CIs using the methods described in Higgins 2011 (Section 7.7.3.2).
Unit of analysis issues
In studies in which participants were randomly assigned to multiple intervention groups, we performed each pair‐wise comparison separately. We did not combine multiple intervention groups in the same analysis.
Dealing with missing data
We attempted to contact the first author or the contact person for each trial with missing data before making a decision about trial eligibility.
Assessment of heterogeneity
Identified trials may not have been carried out according to common protocols, thus introducing differences in participant groups, clinical settings, concomitant care, etc. Important potential sources of heterogeneity include participant characteristics, differences in control or intervention protocols and duration of perioperative fluid optimization.
We described heterogeneity between studies on the basis of participant group, setting and type of intervention. We assessed this statistically when data allowed, using the Chi2 test and the I2 statistic. We investigated important heterogeneity (Chi2 result with a P value < 0.1 and I2 statistic > 50%), when possible, by conducting subgroup analyses and performing meta‐regression.
Assessment of reporting biases
Reporting bias may occur within studies, with certain outcomes not reported. When a report or the original protocol suggested that data on an outcome were collected but were not reported in the paper, we would have contacted study authors to request the data.
If an adequate number of trials had been identified for inclusion, we would have constructed funnel plots and examined them visually to assess the presence of publication bias; we would have used Egger’s test to look for asymmetry.
Data synthesis
We attempted meta‐analysis for outcomes for which we had comparable effect measures from more than one study and when measures of heterogeneity indicated that pooling of results was appropriate. A value of I2 statistic > 80% would argue against presentation of an overall estimate. When we had identified sufficient studies to allow combination of results, differences between studies related to duration and methods of fluid optimization and participant characteristics were likely to suggest that random‐effects models would be the most suitable choice. We used Mantel‐Haenszel models when possible for dichotomous outcomes.
Subgroup analysis and investigation of heterogeneity
If data were sufficient, we investigated the following subgroups, which may account for heterogeneity between studies.
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Duration of monitoring and protocol use.
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Timing of outcome measurement. If the timing of outcome measures varied between studies, we analysed the outcome only in a subgroup analysis.
We assessed any differences in effect size between subgroups in RevMan by using I2 statistical estimates (Higgins 2011).
Sensitivity analysis
When possible, we performed the following sensitivity analyses.
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Re‐analysis excluding studies with a high risk of bias.
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Re‐analysis excluding unpublished studies.
'Summary of findings' table
We used the principles of the GRADE (Grades of Recommendation, Assessment, Development and Evaluation Working Group) (Guyatt 2008) to assess the quality of the body of evidence associated with the specific outcomes in our review.
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All‐cause mortality.
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Length of hospital stay and time to medical fitness for discharge.
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Return to pre‐fracture category of accommodation at six months.
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Return to pre‐fracture mobility at six months.
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Major adverse events in hospital.
We constructed a 'Summary of findings' table by using GRADE software (gradepro.org). The GRADE approach appraises the quality of a body of evidence according to the extent to which one can be confident that an estimate of effect or association reflects the item assessed. The quality of a body of evidence is based on within‐study risk of bias (methodological quality), directness of the evidence, heterogeneity of the data, precision of effect estimates and risk of publication bias.
Results
Description of studies
Results of the search
See Characteristics of included studies; Characteristics of ongoing studies; Characteristics of studies awaiting classification.
We identified 406 records through electronic database searches conducted for the 2015 update. Upon completion of these searches, we included two new studies (Bartha 2013; Moppett 2014) in addition to the three studies included in the previous review update (Brammar 2013); (Schultz 1985; Sinclair 1997; Venn 2002).
We did not identify additional studies for backward citation searching during the 2015 search process. We completed backward citation searching and forward citation searching on the two new studies but identified no additional studies. We searched clinical trials databases using the strategies presented in Appendix 4 and Appendix 5.
See search flow results in Figure 1.
Study flow diagram. Updated search October 2012 to January 2015.
Included studies
We included five studies with 403 randomized participants.
Three trials were conducted in the UK (Moppett 2014; Sinclair 1997; Venn 2002), one in the USA (Schultz 1985) and one in Sweden (Bartha 2013). All provided full‐text publications in the English language. The interval between the first trial and the last trial was approximately 29 years.
All trials included solely adult participants who were undergoing surgery for PFF. Ages of participants ranged from 40 to 102 years. Participants in Bartha 2013 and Moppett 2014 underwent spinal anaesthesia, and those in Sinclair 1997 and Venn 2002 underwent general anaesthesia. Schultz 1985 did not explicitly state the type of anaesthesia, although from the year of this study, we assumed that the clinicians used general anaesthesia. Surgical techniques used to treat PFF included dynamic hip screw, arthroplasty and AO cannulated screws.
Each trial performed different comparisons: goal‐directed haemodynamic treatment (with LiDCO monitor) versus routine fluid treatment based on a treatment algorithm (Bartha 2013); LiDCO monitor‐guided fluid therapy versus standard care (Moppett 2014); Swan‐Ganz monitoring versus CVP monitoring (Schultz 1985); oesophageal Doppler monitoring versus conventional fluid management (Sinclair 1997); and oesophageal Doppler monitoring versus CVP monitoring versus conventional fluid management (Venn 2002). These trial comparisons correspond to the following comparisons in our review: advanced haemodynamic monitoring (LiDCO, Swan‐Ganz, oesophageal Doppler); a protocol using standard measures (CVP monitoring, routine fluid treatment with treatment algorithm); and usual care (standard care, conventional fluid management). Three trials studied only intraoperative fluid optimization (Moppett 2014; Sinclair 1997; Venn 2002); one trial studied intraoperative and postoperative fluid optimization (Bartha 2013); and one trial studied preoperative, intraoperative and postoperative fluid optimization (Schultz 1985).
All trials investigated mortality, although at different time points: at 30 days (Bartha 2013; Moppett 2014), undefined "postoperative" (Schultz 1985) and in‐hospital (Sinclair 1997; Venn 2002). We excluded in‐hospital deaths that occurred more than 30 days postoperatively (Sinclair 1997). On the basis of total hospital stays and ranges reported in Venn 2002, we assumed that all deaths and adverse events in this trial occurred within 30 days of operation. Four trials compared total length of hospital stay (Bartha 2013; Moppett 2014; Sinclair 1997; Venn 2002) and three compared time until medically fit for discharge (Moppett 2014; Sinclair 1997; Venn 2002). One study reported return to pre‐fracture accommodation at six months (Moppett 2014). Across studies, a variety of adverse events were reported, including:
-
cardiovascular (Bartha 2013; Moppett 2014);
-
respiratory (Bartha 2013; Moppett 2014);
-
cerebrovascular (Bartha 2013; Venn 2002);
-
acute kidney failure (Bartha 2013; Moppett 2014);
-
gastrointestinal bleeding (Bartha 2013; Moppett 2014);
-
confusion/delirium (Bartha 2013; Moppett 2014);
-
sepsis (Bartha 2013);
-
deep vein thrombosis (Bartha 2013);
-
wound infection (Bartha 2013; Schultz 1985; Venn 2002);
-
decubitus (Bartha 2013; Schultz 1985);
-
wound haematoma (Bartha 2013);
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abdominal complications (Moppett 2014);
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skin complications (Moppett 2014);
-
pneumonia (Schultz 1985);
-
dislocated prosthesis (Schultz 1985);
-
pneumonitis (Schultz 1985);
-
deep haemorrhage (Venn 2002);
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haematemesis (Venn 2002);
-
chest infection (Venn 2002);
-
urinary tract infection (Venn 2002);
-
cellulitis (Venn 2002);
-
pancreatitis (Venn 2002);
-
pulmonary embolus (Venn 2002);
-
myocardial infarction (Venn 2002);
-
cardiac failure (Venn 2002);
-
rapid atrial fibrillation (Venn 2002);
-
hypotension (Venn 2002);
-
impaired renal function (Venn 2002); and
-
pseudo‐obstruction (Venn 2002).
No studies reported return to pre‐fracture mobility at six months.
Ongoing studies
We identified one ongoing study from clinical trial registers (NCT02382185). This study compares a non‐invasive cardiac monitoring device with usual care and plans to enrol 250 participants. See Characteristics of ongoing studies.
Studies awaiting classification
Two studies are awaiting classification. One study includes a mixed high‐risk surgical population; we have been unable to contact the study authors to request adequate data about participants within the orthopaedic group who were treated for PFF (Sandham 2003). One study appears to be eligible but provided only an abstract with insufficient data. We have not been able to make contact with study authors and await publication of the full report (Vanakas 2012). See Characteristics of studies awaiting classification.
Excluded studies
We excluded 28 full‐text articles identified for further assessment in the 2015 update (see Figure 1). These articles provided the wrong intervention, included the wrong study population or were not RCTs. We have described in the Characteristics of excluded studies table three RCTs that were excluded in the 2015 update because of incorrect intervention or participant group, along with eight RCTs that were excluded in previous versions of this review.
Risk of bias in included studies
We have presented the various bias domains in the ’Risk of bias’ graph and in a ’Risk of bias’ summary figure. We evaluated risk of bias on the basis of major sources of bias (domains), as described above. For a more detailed description of individual trial qualities, see Characteristics of included studies (see Figure 2 and Figure 3).
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Allocation
Three studies adequately reported methods used for random sequence generation (Bartha 2013; Moppett 2014; Venn 2002), and two studies inadequately reported these methods (Schultz 1985; Sinclair 1997). We found baseline differences in Schultz 1985, which suggested that the randomization process may not have been adequate. Therefore, we judged this study to be at a high risk of bias. Bartha 2013 and Moppett 2014 adequately accounted for allocation concealment with the use of external allocation methods, but this information was not reported in Schultz 1985, Sinclair 1997 or Venn 2002.
Blinding
Defining adequate blinding in trials of fluid optimization was challenging. Given the different monitoring techniques described, it was not possible to effectively blind the anaesthetist/clinician. Three trials addressed this by ensuring that the anaesthetist was blinded to measurements from the Doppler and LiDCO monitors in the control groups (Bartha 2013; Moppett 2014; Sinclair 1997); however, anaesthetists remained aware of group allocation, and we were unclear about whether this may have affected their performance. Therefore, we judged all trials to be at unclear risk of performance bias.
Three studies had made adequate attempts to blind nursing personnel and/or those collecting outcome data to reduce risk of detection bias (Moppett 2014; Sinclair 1997; Venn 2002). Although data on the LiDCO monitor were coded, personnel collecting relevant outcome data for the review outcomes were not blinded in Bartha 2013, and we judged this study to be at a high risk of detection bias. Venn 2002 reported no details of data collection.
Incomplete outcome data
Two trials reported complete follow‐up for mortality, morbidity, adverse events and length of stay (Sinclair 1997; Venn 2002). One trial provided no information about exclusions due to deviations from protocol (Schultz 1985) but reported no participants lost to follow‐up. One study (Moppett 2014) reported a high number of losses (12%), and although study authors provided explanations, we considered this number of losses to have a potential influence on effect estimates. Bartha 2013 explained losses similarly but described fewer losses.Both Bartha 2013 and Venn 2002 performed intention‐to‐treat (ITT) analysis on lost data.
Selective reporting
Both Bartha 2013 and Moppett 2014 were registered with clinical trials registers, and we were able to compare protocols against published study reports. Moppett 2014 had also published a protocol in an open access journal (Wiles 2011). Bartha 2013 reported all outcomes relevant to this review, and we judged this study to be at low risk of bias; however, some long‐term outcomes on health‐related quality of life were reported in an interim publication but not in the full study publication. Similarly, we found that Moppett 2014 reported the outcomes relevant to this review, although study authors did not report data on cost of care nor on changes in the way the heart and blood vessels work with spinal anaesthesia. We judged both of these studies as having low risk of bias.
The remaining three trials were not registered, and we were unable to source any published protocols for them. Two of these trials reported all expected outcomes in their Methods section (Sinclair 1997; Venn 2002). One trial provided inadequate information about expected outcomes; therefore, we assessed the risk of selective reporting as unclear (Schultz 1985).
Some of our analyses were subject to limitations because length of stay data were published in graphical form without adequate corresponding numerical data. One trial did not provide details on length of follow‐up in terms of mortality but provided sufficient data for analysis (Sinclair 1997); data regarding morbidity and adverse events were adequate in all studies.
Other potential sources of bias
Three studies received funding that appeared to be independent and unlikely to influence the results (Bartha 2013; Moppett 2014; Sinclair 1997). Four trials reported sample size calculations (Bartha 2013; Moppett 2014; Sinclair 1997; Venn 2002). One trial was stopped early because of difficulty in recruiting participants (Bartha 2013). Trials were too few to permit construction of funnel plots to facilitate assessment of publication bias, or to allow performance of Egger's test for asymmetry.
Analyses of the benefits of fluid optimization in this group of participants were limited by differences in study design. Three trials involved intraoperative optimization (Moppett 2014; Sinclair 1997; Venn 2002). One involved both intraoperative and postoperative optimization (Bartha 2013), and one involved preoperative, intraoperative and postoperative optimization (Schultz 1985). We noted differences between trials in outcome definitions, in time points for mortality and length of stay reporting and in types of adverse events reported. In addition, all trials involved relatively few participants.
Effects of interventions
See: Summary of findings for the main comparison Advanced haemodynamic monitoring compared with protocol using standard measures such as CVP for proximal femoral fracture; Summary of findings 2 Advanced haemodynamic monitoring compared with usual care for perioperative fluid optimization; Summary of findings 3 Protocol using standard measures such as CVP compared with usual care for perioperative fluid optimization
See also summary of findings Table for the main comparison; summary of findings Table 2; summary of findings Table 3.
Comparison 1. Advanced haemodynamic monitoring versus protocol using standard measures
All‐cause mortality
Three trials reported mortality (Bartha 2013; Schultz 1985; Venn 2002). For one study, the follow‐up period was unclear but was reported as "postoperative" (Schultz 1985). This trial showed a significant reduction in mortality (risk ratio (RR) 0.1, 95% confidence interval (CI) 0.01 to 0.74; 70 participants); however, we had serious concerns about its quality. In Venn 2002, the time frame for death was described as within hospital and results were consistent with both increased and decreased risk of mortality in the intervention group (RR 0.52, 95% CI 0.14 to 1.88; 61 participants). In Bartha 2013, the time frame for death was within 30 days, and again results were consistent with both increased and decreased risk of mortality in the intervention group (RR Mantel‐Haenszel (M‐H) random‐effects 0.76, 95% CI 0.18 to 3.28; 149 participants). See Analysis 1.1 for pooled data that showed no statistically significant differences between groups (RR M‐H random‐effects 0.41, 95% CI 0.14 to 1.20; 280 participants). Using GRADE (Grades of Recommendation, Assessment, Development and Evaluation Working Group), we downgraded the quality of this evidence to very low, as few studies were identified by which we could judge precision and we noted a high level of bias in Schultz 1985 and some inconsistency in the study results (see summary of findings Table for the main comparison).
Length of hospital stay
Total length of hospital stay
Two trials reported length of hospital stay (Bartha 2013; Venn 2002) by providing data as median and interquartile range (Bartha 2013) and as mean and 95% CI (Venn 2002). Therefore, it was not possible for review authors to pool these data. Bartha 2013 (149 participants) reported median length of hospital stay as nine (range, three to 20) days in the advanced haemodynamic monitoring group and as 10 (range one to 38) days in the protocol group. Venn 2002 (61 participants) reported mean length of hospital stay as 13.5 (95% CI 10.9 to 17.5) days in the advanced haemodynamic monitoring group and as 13.3 (95% CI 10.3 to 19.2) days in the protocol group. Study authors did not report these data as statistically significant. Using GRADE, we downgraded the quality of this evidence to low, as studies were few and both studies reported a wide range and confidence interval (see summary of findings Table for the main comparison).
Time to medical fitness for discharge
Venn 2002 also reported time to medical fitness as mean 7.8 (95% CI 5.9 to 12.3) days in the advanced haemodynamic monitoring group and 10.0 (95% CI 8.1 to 12.0) days in the protocol group. Study authors reported this finding as statistically significant (P value = 0.035). No other study reported this outcome. We also downgraded the quality of this evidence to low because data were available for only one study (see summary of findings Table for the main comparison).
Return of participant to pre‐fracture category of accommodation at six months; return to pre‐fracture mobility at six months
No trial reported data for these outcomes.
Major adverse events in‐hospital
Three trials investigated complications, reporting overall morbidity and cardiovascular or neurological outcomes; however, iatrogenic events were not reported clearly by intervention/control groups (Bartha 2013; Schultz 1985; Venn 2002), and cardiovascular and neurological events were reported in such a way that it was not clear whether participants had experienced more than one event. These results are presented in Table 1. We combined the numbers of participants with one or more complication in Analysis 1.2, which shows no statistically significant differences between the advanced haemodynamic monitoring group and those using a protocol (RR M‐H random‐effects 0.83, 95% CI 0.59 to 1.17; 280 participants). Using GRADE, we downgraded the quality of this evidence to very low, as again studies were too few to allow us to judge precision, level of bias in Schultz 1985 was high and results showed some inconsistency (see summary of findings Table for the main comparison).
| Study ID | Adverse events | Advanced haemodynamic monitoring | Protocol using standard measures |
| n = 74 | n = 75 | ||
| Cardiopulmonary | Cardiovascular 5 Respiratory 5 | Cardiovascular 6 Respiratory 7 | |
| Neurological | Cerebrovascular 0 Confusion 3 | Cerebrovascular 2 Confusion 6 | |
| Other | Acute kidney failure 1 Gastrointestinal bleeding 0 Sepsis 2 Deep vein thrombosis 0 Wound infection 2 Delayed healing 0 Urinary tract infection 16 Decubitus 6 Wound haematoma 0 Other 4 | Acute kidney failure 1 Gastrointestinal bleeding 0 Sepsis 0 Deep vein thrombosis 0 Wound infection 1 Delayed healing 0 Urinary tract infection 12 Decubitus 1 Wound haematoma 1 Other 6 | |
| n = 35 | n = 35 | ||
| Other | Pneumonia 1 Wound infection 1 Pneumonitis 1 | Pneumonia 2 Decubitis ulcer 1 | |
| n = 30 | n = 31 | ||
| Cardiopulmonary | Chest infection 2 Pulmonary embolus 1 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 3 Hypotension 0 | Chest infection 3 Pulmonary embolus 0 Myocardial infarction 1 Cardiac failure 1 Rapid atrial fibrillation 1 Hypotension 0 | |
| Neurological | Cerebrovascular accident 2 | Cerebrovascular accident 1 | |
| Other | Deep haemorrhage 1 Haematemesis 0 Wound infection 0 Urinary tract infection 2 Cellulitis 0 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 0 | Deep haemorrhage 0 Haematemesis 0 Wound infection 0 Urinary tract infection 1 Cellulitis 1 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 1 |
Comparison 2. Advanced haemodynamic monitoring versus usual care
All‐cause mortality
Two trials reported in‐hospital mortality (Sinclair 1997; Venn 2002), and one trial reported mortality at 30 days (Moppett 2014). We excluded two deaths from Sinclair 1997 ‐ one each from the intervention and control groups ‐ as they occurred more than 30 days postoperatively. Pooled results are consistent with both increased and decreased risks of mortality in participants who received advanced haemodynamic monitoring (RR M‐H random‐effects 0.45, 95% CI 0.07 to 2.95; 213 participants). See Analysis 2.1. Using GRADE, we downgraded the quality of this evidence to low, as studies were few (see summary of findings Table 2).
Length of hospital stay
Total length of hospital stay
Three trials investigated total length of hospital stay (Moppett 2014; Sinclair 1997; Venn 2002). In Moppett 2014 (114 participants), the total mean length of stay was 15.3 (95% CI 13.8 to 17.2) days in the intervention group and 14.2 (95% CI 12.9 to 15.8) days in the usual care group.
For Venn 2002 (59 participants), we used the RevMan calculator to determine the mean difference between groups for length of hospital stay. We found no significant difference between the advanced haemodynamic monitoring group and the usual care group (4.00 days shorter, 95% CI 11.65 days shorter to 3.65 days longer). This result differs from previously reported in Brammar 2013 (see Differences between protocol and review).
We carried out meta‐analysis on these two studies, using the RevMan calculator to calculate standard deviations. Results showed no difference between groups for overall length of hospital stay (MD IV fixed 0.63, 95% CI ‐1.70 to 2.96). See Analysis 2.2.
Sinclair 1997 (40 participants) provided data in the form of median and interquartile ranges, which were not suitable for inclusion in a meta‐analysis, but reported a reduction of eight days in total hospital stay (from 20 to 12); study authors reported a significant difference at P value < 0.05 (Mann‐Whitney U test). Using GRADE, we downgraded the quality of this evidence to low, as studies were few (see summary of findings Table 2).
Time to medical fitness for discharge
Three trials investigated time to medical fitness for discharge (Moppett 2014; Sinclair 1997; Venn 2002). Moppett 2014 (114 participants) reported no difference between groups in time to medical fitness for discharge, with a mean of 13.1 (95% CI 11.9 to 14.5) days in the intervention group and 12.2 (95% CI 11.1 to 13.5) days in the usual care group. Venn 2002 (59 participants) described a reduction in time to medical fitness for discharge in the advanced haemodynamic group (6.20 days shorter, 95% CI 11.01 to 1.39 days shorter).
We carried out meta‐analysis on these two studies, using the RevMan calculator to calculate standard deviations. Results showed no differences between groups in the number of days needed for participants to be medically fit for discharge (MD IV fixed 0.01, 95% CI ‐1.74 to 1.71). See Analysis 2.3. We noted a high level of statistical heterogeneity for this effect estimate (I2 = 86%).
Sinclair 1997 (40 participants) provided data in the form of medians and interquartile ranges, which were not suitable for inclusion in a meta‐analysis, but reported a reduction of five days in median time to fitness for discharge (from 15 days to 10 days); study authors reported a significant difference at P value < 0.05 (Mann‐Whitney U test). Using GRADE, we downgraded the quality of this evidence to low, as studies were few (see summary of findings Table 2).
Return of participant to pre‐fracture category of accommodation at six months; return to pre‐fracture mobility at six months
Moppett 2014 reported on the number of participants returning to their normal accommodation after discharge. They noted no statistically significant difference between groups, with 23 of 51 in the group monitored with LiDCO and 25 of 63 in the group receiving usual care. Using GRADE, we downgraded the quality of this evidence to low, as we identified only one study (see summary of findings Table 2).
Major adverse events in hospital
Two trials (Moppett 2014; Venn 2002) reported these. Moppett 2014 reported two types of cardiopulmonary complications, and Venn 2002 reported three types. However, these results were not reported by participant; therefore, overall denominator figures were unclear. These results, along with all reported complications, are presented in Table 2. We combined neurological outcomes in Analysis 2.4, including the number of participants having acute delirium in Moppett 2014 and the number having a cerebrovascular accident in Venn 2002. The data for the number of participants developing one or more complications was combined in Analysis 2.4, noting no statistically significant differences between those receiving advanced haemodynamic monitoring and control groups (M‐H RR random‐effects 0.72, 95% CI 0.40 to 1.31; 173 participants). Using GRADE, we downgraded the quality of this evidence to low, as studies were few (see summary of findings Table 2).
| Study ID | Adverse events | Advanced haemodynamic monitoring | Usual care |
| n = 51 | n = 63 | ||
| Cardiopulmonary | Cardiovascular 8 Respiratory 0 | Cardiovascular 6 Respiratory 0 | |
| Neurological | Acute delirium 11 | Acute delirium 13 | |
| Other | Infectious 21 Abdominal 2 Bleeding 0 Skin 0 Renal (RIFLE) 18* Other 3 | Infectious 34 Abdominal 1 Bleeding 0 Skin 0 Renal (RIFLE) 32* Other 3 | |
| n = 30 | n = 29 | ||
| Cardiopulmonary | Chest infection 2 Pulmonary embolus 1 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 3 Hypotension 0 | Chest infection 5 Pulmonary embolus 0 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 2 Hypotension 3 | |
| Neurological | Cerebrovascular accident 2 | Cerebrovascular accident 1 | |
| Other | Deep haemorrhage 1 Haematemesis 0 Wound infection 0 Urinary tract infection 2 Cellulitis 0 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 0 | Deep haemorrhage 1 Haematemesis 1 Wound infection 2 Urinary tract infection 3 Cellulitis 0 Pancreatitis 1 Hypotension 3 Impaired renal function 2 Pseudo‐obstruction 0 |
*RIFLE scores sum of patients at risk, injury or failure
Comparison 3. Protocol using standard measures versus usual care
All‐cause mortality
Only one trial reported on this outcome (Venn 2002) and found no difference in mortality between participants who received care according to the protocol and those given standard care (RR 2.81, 95% CI 0.61 to 12.81; 60 participants). See Table 3.
| Outcomes reported in Venn 2002: comparison 3 | Protocol ‐ CVP N = 31 | Standard care
N = 29 | Effect estimate (95% CI) | ||
|
| Mean | SD | Mean | SD | Mean difference |
| Length of hospital stay (days) | 13.3 | 12.1 | 17.5 | 13.8 | ‐4.20 (‐11.0 to 2.60) |
| Time to fitness to discharge | 10 | 5.3 | 13.9 | 6.6 | ‐3.90 (‐7.05 to ‐0.75) |
|
|
|
|
|
|
|
|
| Events |
| Events |
| MH relative risk |
| Mortality | 6 |
| 2 |
| 2.81 (0.61 to 12.81) |
| Adverse events |
|
|
|
|
|
| · Cardiopulmonary ‐ episodes | 6 |
| 7 |
| N/A |
| · Neurological ‐ participants | 1 |
| 1 |
| 0.94 (0.06 to 14.27) |
| · Any, including minor ‐ participants | 8 |
| 14 |
| 0.53 (0.26 to 1.08) |
Length of hospital stay
Total length of hospital stay and Time to medical fitness for discharge
One trial reported a reduction in time to medical fitness (3.9 days shorter, 95% CI 7.05 to 0.75 days shorter; 60 participants) but not in total hospital stay (4.2 days shorter, 95% CI 11.0 days shorter to 2.60 days longer; 60 participants) (Venn 2002; see Table 3).
Return of participant to pre‐fracture category of accommodation at six months; return to pre‐fracture mobility at six months
No trial reported data for these outcomes.
Major adverse events in hospital
Only one trial reported these, and results were consistent with increased and decreased risk in participants who had received care according to a protocol for neurological events (RR 0.94, 95% CI 0.06 to 14.27; 60 participants) and for all complications (RR 0.53, 95% CI 0.26 to 1.08; 60 participants) (Venn 2002; see Table 3).
Subgroup and sensitivity analyses
No more than three studies were available for inclusion in any of the comparison groups, and it was not possible to perform meaningful subgroup analyses.
We were particularly concerned about the high risk of bias assessed for Schultz 1985. We removed this study from Analysis 1.1 and Analysis 1.2, with no effect on the results.
We obtained no unpublished data; therefore, it was not possible to carry out this sensitivity analysis.
Discussion
Summary of main results
We included in this review five studies with 403 participants.
The conclusions of this updated review remain the same as those of both the original review and the last update (Brammar 2013; Price 2004). We found no benefit for the use of fluid optimization strategies in participants undergoing surgery for proximal femoral fracture (PFF) in terms of rates of mortality or adverse events (Brammar 2013; Price 2004). In the previous review update, one study had presented data indicating a reduction in length of stay (Venn 2002). However, with the additional studies included in this update, we found no consistent evidence for this outcome. One study reported a reduction in time to medical fitness when advanced haemodynamic monitoring was compared with a protocol using standard monitoring, and when a protocol using standard monitoring was compared with usual care (Venn 2002).
Overall completeness and applicability of evidence
We conducted a thorough search for the 2015 update, carrying out both backward and forward citation tracking of included studies. We used the search strategies delineated in a previous version of this review (Brammar 2013). All five studies included here provided evidence applicable to participants with PFF, with appropriate comparisons of fluid optimization techniques.
Applicability may extend only to those countries in which included studies were conducted, as usual care, fluid management and perioperative care of patients with femoral fracture may differ considerably between countries. Management of this patient group may also differ within countries, with different institutions adopting different protocols; with such few included studies, it could be argued that applicability of the review is limited by institution.
Quality of the evidence
We noted differences in quality among our included studies. We were concerned about the robustness of the randomization process in one study and judged this study to have a high risk of bias (Schultz 1985). We included this study in our analysis but used GRADEpro software to downgrade the overall quality of evidence for relevant outcomes accordingly. Blinding of clinicians to group allocation was an inevitable challenge in this review. However, we were unable to make a decision about whether this had introduced performance bias to our review.
Our main concern was lack of data because we identified few studies with a small number of participants for each of our comparisons. It is arguable that any mortality reduction due to the interventions described in our review would be small because of the many other factors that put participants with PFF at relatively high risk of death. If in‐hospital mortality of 6.6% is assumed (Moppett 2012), a study with 80% power to detect a 50% decrease in in‐hospital mortality (from 6.6% to 3.3%) would require randomization of 678 participants into each group (α = 0.05). Therefore, much larger studies than the ones presented in this review are likely needed to show the benefit derived from these interventions. Similarly, to detect a 50% reduction in adverse event incidence (from 15% to 7.5%) (Lawrence 2002; Roche 2005), 278 participants would be required for each group. We used GRADEpro to downgrade the quality of most outcomes to low or very low, as the review could not offer precision for our objectives.
Potential biases in the review process
To the best of our knowledge, no potential biases arose from the review process.
Agreements and disagreements with other studies or reviews
The authors of the original version of this review stated that invasive methods of fluid optimization may shorten hospital stay, but their effects on other important, patient‐centred, longer‐term outcomes are uncertain. We would agree in general with this but urge caution in interpretation of hospital stay data that are limited in scale and in some cases are not adequate for detailed analysis. We are not aware of any other good quality studies or systematic reviews investigating perioperative fluid optimization after PFF.
Study flow diagram. Updated search October 2012 to January 2015.
Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Comparison 1 Advanced haemodynamic monitoring versus protocol using standard measures, Outcome 1 All‐cause mortality.
Comparison 1 Advanced haemodynamic monitoring versus protocol using standard measures, Outcome 2 Adverse outcomes.
Comparison 2 Advanced haemodynamic monitoring versus usual care, Outcome 1 All‐cause mortality.
Comparison 2 Advanced haemodynamic monitoring versus usual care, Outcome 2 Total length of hospital stay.
Comparison 2 Advanced haemodynamic monitoring versus usual care, Outcome 3 Days until medically fit for discharge.
Comparison 2 Advanced haemodynamic monitoring versus usual care, Outcome 4 Adverse outcomes.
| Advanced haemodynamic monitoring compared with protocol using standard measures such as CVP for proximal femoral fracture | ||||||
| Patient or population: patients with proximal femoral fracture | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Protocol using standard measures such as CVP | Advanced haemodynamic monitoring | |||||
| All‐cause mortality | Study population | RR 0.41 | 280 | ⊕⊝⊝⊝ | ||
| 142 per 1000 | 58 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Not estimabled | 203 | ⊕⊕⊝⊝ | Data reported as median (range) in Bartha 2013 and as mean (95% confidence interval) in Venn 2002 | ||
| Medically fit for discharge | Mean medically fit for discharge in the intervention groups was | 90 | ⊕⊕⊝⊝ | |||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Not estimable | ‐ | Not reported | |||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | |||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Not estimable | 0 | ||||
| Adverse outcomes ‐ all | Study population | RR 0.90 | 280 | ⊕⊝⊝⊝ | ||
| 319 per 1000 | 287 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aConcerns about randomization process in Schultz 1985; high risk of selection bias eData from 1 study only. Downgraded 2 levels | ||||||
| Advanced haemodynamic monitoring compared with usual care for perioperative fluid optimization | ||||||
| Patient or population: patients with perioperative fluid optimization | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Usual care | Advanced haemodynamic monitoring | |||||
| All‐cause mortality | Study population | RR 0.45 | 213 | ⊕⊕⊝⊝ | ||
| 89 per 1000 | 40 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Mean total length of hospital stay in the control groups was | Mean total length of hospital stay in the intervention groups was | 175 | ⊕⊕⊝⊝ | ||
| Medically fit for discharge | Mean medically fit for discharge in the control groups was | Mean medically fit for discharge in the intervention groups was | 175 | ⊕⊕⊝⊝ | ||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Study population | Not estimable | 114 | ⊕⊕⊝⊝ | ||
| 397 per 1000 | 0 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | |||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Study population | RR 1.10 | 173 | ⊕⊕⊝⊝ | ||
| 152 per 1000 | 170 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ all | Study population | RR 0.78 | 173 | ⊕⊕⊝⊝ | ||
| 554 per 1000 | 432 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aConfidence intervals cross no effect and are consistent with increased as well as decreased risk. Downgraded 1 level | ||||||
| Protocol using standard measures such as CVP compared with usual care for perioperative fluid optimization | ||||||
| Patient or population: patients with perioperative fluid optimization | ||||||
| Outcomes | Illustrative comparative risks* (95% CI) | Relative effect | Number of participants | Quality of the evidence | Comments | |
| Assumed risk | Corresponding risk | |||||
| Usual care | Protocol using standard measures such as CVP | |||||
| All‐cause mortality | Study population | RR 2.81 | 60 | ⊕⊕⊝⊝ | ||
| 69 per 1000 | 194 per 1000 | |||||
| Moderate | ||||||
| Total length of hospital stay | Mean total length of hospital stay in the control groups was | Mean total length of hospital stay in the intervention groups was | 60 | ⊕⊕⊝⊝ | ||
| Medically fit for discharge | Mean medically fit for discharge in the intervention groups was | 60 | ⊕⊕⊕⊝ | |||
| Return to pre‐fracture accommodation/return to pre‐fracture mobility | Not estimable | ‐ | Not reported | |||
| Adverse outcomes ‐ cardiopulmonary | Study population | Not estimable | 0 | Not reported | ||
| Moderate | ||||||
| Adverse outcomes ‐ neurological | Study population | RR 0.94 | 60 | ⊕⊕⊝⊝ | ||
| 34 per 1000 | 32 per 1000 | |||||
| Moderate | ||||||
| Adverse outcomes ‐ all | Study population | RR 0.53 | 60 | ⊕⊕⊝⊝ | ||
| 483 per 1000 | 256 per 1000 | |||||
| Moderate | ||||||
| *The basis for the assumed risk (e.g. median control group risk across studies) is provided in footnotes. The corresponding risk (and its 95% confidence interval) is based on the assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI) | ||||||
| GRADE Working Group grades of evidence | ||||||
| aBased on 1 study with a small number of events. Wide confidence intervals consistent with increased as well as decreased risk. Downgraded by 1 level | ||||||
| Study ID | Adverse events | Advanced haemodynamic monitoring | Protocol using standard measures |
| n = 74 | n = 75 | ||
| Cardiopulmonary | Cardiovascular 5 Respiratory 5 | Cardiovascular 6 Respiratory 7 | |
| Neurological | Cerebrovascular 0 Confusion 3 | Cerebrovascular 2 Confusion 6 | |
| Other | Acute kidney failure 1 Gastrointestinal bleeding 0 Sepsis 2 Deep vein thrombosis 0 Wound infection 2 Delayed healing 0 Urinary tract infection 16 Decubitus 6 Wound haematoma 0 Other 4 | Acute kidney failure 1 Gastrointestinal bleeding 0 Sepsis 0 Deep vein thrombosis 0 Wound infection 1 Delayed healing 0 Urinary tract infection 12 Decubitus 1 Wound haematoma 1 Other 6 | |
| n = 35 | n = 35 | ||
| Other | Pneumonia 1 Wound infection 1 Pneumonitis 1 | Pneumonia 2 Decubitis ulcer 1 | |
| n = 30 | n = 31 | ||
| Cardiopulmonary | Chest infection 2 Pulmonary embolus 1 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 3 Hypotension 0 | Chest infection 3 Pulmonary embolus 0 Myocardial infarction 1 Cardiac failure 1 Rapid atrial fibrillation 1 Hypotension 0 | |
| Neurological | Cerebrovascular accident 2 | Cerebrovascular accident 1 | |
| Other | Deep haemorrhage 1 Haematemesis 0 Wound infection 0 Urinary tract infection 2 Cellulitis 0 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 0 | Deep haemorrhage 0 Haematemesis 0 Wound infection 0 Urinary tract infection 1 Cellulitis 1 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 1 |
| Study ID | Adverse events | Advanced haemodynamic monitoring | Usual care |
| n = 51 | n = 63 | ||
| Cardiopulmonary | Cardiovascular 8 Respiratory 0 | Cardiovascular 6 Respiratory 0 | |
| Neurological | Acute delirium 11 | Acute delirium 13 | |
| Other | Infectious 21 Abdominal 2 Bleeding 0 Skin 0 Renal (RIFLE) 18* Other 3 | Infectious 34 Abdominal 1 Bleeding 0 Skin 0 Renal (RIFLE) 32* Other 3 | |
| n = 30 | n = 29 | ||
| Cardiopulmonary | Chest infection 2 Pulmonary embolus 1 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 3 Hypotension 0 | Chest infection 5 Pulmonary embolus 0 Myocardial infarction 0 Cardiac failure 0 Rapid atrial fibrillation 2 Hypotension 3 | |
| Neurological | Cerebrovascular accident 2 | Cerebrovascular accident 1 | |
| Other | Deep haemorrhage 1 Haematemesis 0 Wound infection 0 Urinary tract infection 2 Cellulitis 0 Pancreatitis 0 Hypotension 0 Impaired renal function 0 Pseudo‐obstruction 0 | Deep haemorrhage 1 Haematemesis 1 Wound infection 2 Urinary tract infection 3 Cellulitis 0 Pancreatitis 1 Hypotension 3 Impaired renal function 2 Pseudo‐obstruction 0 | |
| *RIFLE scores sum of patients at risk, injury or failure | |||
| Outcomes reported in Venn 2002: comparison 3 | Protocol ‐ CVP N = 31 | Standard care
N = 29 | Effect estimate (95% CI) | ||
|
| Mean | SD | Mean | SD | Mean difference |
| Length of hospital stay (days) | 13.3 | 12.1 | 17.5 | 13.8 | ‐4.20 (‐11.0 to 2.60) |
| Time to fitness to discharge | 10 | 5.3 | 13.9 | 6.6 | ‐3.90 (‐7.05 to ‐0.75) |
|
|
|
|
|
|
|
|
| Events |
| Events |
| MH relative risk |
| Mortality | 6 |
| 2 |
| 2.81 (0.61 to 12.81) |
| Adverse events |
|
|
|
|
|
| · Cardiopulmonary ‐ episodes | 6 |
| 7 |
| N/A |
| · Neurological ‐ participants | 1 |
| 1 |
| 0.94 (0.06 to 14.27) |
| · Any, including minor ‐ participants | 8 |
| 14 |
| 0.53 (0.26 to 1.08) |
| Outcome or subgroup title | No. of studies | No. of participants | Statistical method | Effect size |
| 1 All‐cause mortality Show forest plot | 3 | 280 | Risk Ratio (M‐H, Random, 95% CI) | 0.41 [0.14, 1.20] |
| 2 Adverse outcomes Show forest plot | 3 | 280 | Risk Ratio (M‐H, Random, 95% CI) | 0.83 [0.59, 1.17] |
| 2.1 Any complications, including minor | 3 | 280 | Risk Ratio (M‐H, Random, 95% CI) | 0.83 [0.59, 1.17] |
| Outcome or subgroup title | No. of studies | No. of participants | Statistical method | Effect size |
| 1 All‐cause mortality Show forest plot | 3 | 213 | Risk Ratio (M‐H, Random, 95% CI) | 0.45 [0.07, 2.95] |
| 2 Total length of hospital stay Show forest plot | 2 | 173 | Mean Difference (IV, Fixed, 95% CI) | 0.63 [‐1.70, 2.96] |
| 3 Days until medically fit for discharge Show forest plot | 2 | 173 | Mean Difference (IV, Fixed, 95% CI) | ‐0.01 [‐1.74, 1.71] |
| 4 Adverse outcomes Show forest plot | 2 | Risk Ratio (M‐H, Random, 95% CI) | Subtotals only | |
| 4.1 Neurological | 2 | 173 | Risk Ratio (M‐H, Random, 95% CI) | 1.10 [0.56, 2.18] |
| 4.2 Any complications, including minor | 2 | 173 | Risk Ratio (M‐H, Random, 95% CI) | 0.72 [0.40, 1.31] |
