Chapter 48 Endocrine Quality Registers
Surgical Outcome Measurement
Measurement of Quality: The Science of Improvement
In 1990, the Institute of Medicine defined quality as “the degree to which Health Services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”1 Most doctors regard providing high-quality care as not only a professional responsibility but also as their raison d’être. For surgeons, the focus is on patient/populations and clinical effectiveness and safety. However, the wider dimensions of quality such as efficiency, equity, patient-responsiveness, access and coordination must not be overlooked.2 Audit is a major component of the science of improvement, and a reliable system of measuring outcomes has many benefits:
• Affords greater public transparency and accountability
• Enables surgeons with a better basis for judging and improving their practice
• Offers patients the basis to make informed choices about their care
• Provides evidence of service improvement and quality assurance of operations
• Delivers better data for the health service commissioners who make funding decisions
• Is a powerful tool to evaluate new diagnostic and surgical techniques
Surgical Registries
A registry is defined as a systematic collection of a clearly defined set of health and demographic data for patients with specific health characteristics, held in a central database for a predefined purpose.3 Compared to clinical trials, registries try to include the whole population with a certain disease or operation without any selection criteria. The disadvantage of this form of research is that the amount of data collected is small compared with clinical trials. However, registries aim to obtain a complete and unbiased overview of all cases. There are two main outcomes of a registry:
There are excellent examples of local endocrine databases which have produced data that have brought about improvements in the surgical management of endocrine disease.4,5 One of the best examples of long-term data acquisition that covers an entire nation is the Swedish Cancer Registries, which has made long-term population-based research possible, for example, on prognostic factors and survival rates in differentiated thyroid cancer.6 However, there are fewer examples of registries that include surgical treatment, the exception being the Society for Cardiothoracic Surgeons of Great Britain and Ireland.7 The principal advantage of running such an initiative at a national level is that it enables individual surgeons to benchmark their own practice with their peers.
Steps in the Development of a Registry
The most important step is defining a sensible minimum data set (Table 48-1). It cannot be emphasized enough that there is a balance to be achieved between collecting sufficient data and avoiding the creation of an unwieldy system that takes too long to enter the data and risks losing compliance from the local centers. The data can be used for multiple purposes (Figure 48-1). In fields of surgery where mortality and major morbidity are a significant feature, it is important to establish a risk-adjusted benchmark for clinical outcomes, which addresses the patient’s health status or functional ability such as the Physiological and Operative Severity Score for the enUmeration of Mortality and morbidity (POSSUM) and National Surgical Quality Improvement Program (NSQIP).8 For thyroid and parathyroid surgery, this detail is probably unnecessary so long as variables including age, reoperative surgery, pathology, and retrosternal extension (for thyroid) are recorded. Another key step is to choose a software company that can provide appropriate technology to minimize errors and maximize the efficiency of obtaining results. Nowadays this includes Internet capability as well as data collection and interrogation. There are two types of data errors: systematic and random.9 Systematic errors are caused by programming errors, unclear definitions for data items, or violation of the data collection protocol. The software should therefore be designed to be user-friendly and should include definitions whenever possible. Drop-down boxes and “hover” buttons are particularly useful in this regard. Random errors are caused by inaccurate data transcription and typing errors. These can be minimized by using ambiguity-free text.
Data Set | Examples (Thyroid) |
---|---|
Demographics | Age, gender |
Risk factors | Reoperative surgery, retrosternal goiter |
Clinical history | Endocrine status, compressive symptoms, hereditary disease |
Pre-op investigations | Vocal cord check, cytology |
Operation details | Principal surgeon, nodal surgery, use of nerve monitor, extent of surgery |
Pathology | Primary pathology, incidental pathology |
Early complications | Hypocalcemia, hemorrhage |
Long-term outcomes | Voice change, hypocalcemia |
Maintaining Quality
The value of a registry depends on the quality of the data. The two principal attributes of a registry that underpin quality are data accuracy and data completeness. Although it is unrealistic to aim for a registry database that is completely free of errors, there are some clear principles that will help to reduce the accuracy and completeness of data entry9:
• Collect the data in space close to the original data source as soon as the data are available
• Encourage data entry by the clinician or directly from a relevant electronic data source
Confidence in a registry is determined by the validity of the data. Validation is best tested by some form of external control. The Scandinavian registry (discussed later) undertakes random audit checks via an unbiased third party who has been appointed by the central coordinating unit. The results of these checks are given to both the local center(s) and the central registry group. An alternative method would be to correlate outcomes measured in the registry with data collected via alternative national initiatives (e.g., HES data in the UK National Health Service [NHS]).10
Current Status (Results)
Thyroid Surgery
The similarity of the demographics is striking (Figure 48-2). The majority of patients are female (81% SQR, 82.9% UK).
The Voice and Recurrent Laryngeal Nerve Injury
In SQR, a postoperative vocal cord check was not carried out in 51.3% (up to 6 weeks’ postoperation). Postoperative vocal cord paresis was documented in 4.3% of operated patients (3% of “nerves at risk”) and occurred twice as often on the right side compared with the left. Only in 11% of the patients with postoperatively documented vocal cord paresis did the surgeon intraoperatively recognize recurrent laryngeal nerve damage.11 Furthermore, postoperative vocal cord paresis was diagnosed almost twice as often (OR 1.92) in units that used postoperative laryngoscopy routinely (regardless of voice changes). At the 6-month postoperative recheck, residual recurrent paresis was reported in 0.9%. However, the 6-month data are incomplete and the actual frequency of postoperative unilateral vocal cord paresis after 6 months is most likely in the region of 1.5%.
UK data are not collected in such a detailed manner. The rate of postoperative laryngoscopy is even lower (21.5%), so this complication is likely to be underreported. Nevertheless the rate of new proved recurrent laryngeal nerve (RLN) palsy is 2.5% (27/1068). In their present form, the data cannot be adjusted for “nerves at risk,” but the rate of RLN injury for total thyroidectomy is double that of a lobectomy. When the RLN injury rate was grouped according to surgeon volume, the data provide evidence that surgeons undertaking more than 50 cases per year have a lower RLN injury rate (Table 48-2).
Annual Case Load | RLN Injury Rate |
---|---|
< 11 | 4.2% |
11-25 | 3.1% |
26-50 | 4.0% |
> 50 | 0.9% |