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Biomedical subjects

David R Goldhill

Publications and source records attributed to David R Goldhill.

6 recordsLinked to original sources
PubMed2007

Rotational bed therapy to prevent and treat respiratory complications: a review and meta-analysis.

BACKGROUND: Immobility is associated with complications involving many body systems. OBJECTIVE: To review the effect of rotational therapy (use of therapeutic surfaces that turn on their longitudinal axes) on prevention and/or treatment of respiratory complications in critically ill patients. METHODS: Published articles evaluating prophylaxis and/or treatment were reviewed. Prospective randomized controlled trials were assessed for quality and included in meta-analyses. RESULTS: A literature search yielded 15 nonrandomized, uncontrolled, or retrospective studies. Twenty prospective randomized controlled trials on rotational therapy were published between 1987 and 2004. Various types of beds were studied, but few details on the rotational parameters were reported. The usual control was manual turning of patients by nurses every 2 hours. One animal investigation and 12 clinical trials addressed the effectiveness of rotational therapy in preventing respiratory complications. Significant benefits were reported in the animal study and 4 of the trials. Significant benefits to patients were reported in 2 of another 4 studies focused on treatment of established complications. Researchers have examined the effects of rotational therapy on mucus transport, intrapulmonary shunt, hemodynamic effects, urine output, and intracranial pressure. Little convincing evidence is available, however, on the most effective rotation parameters (eg, degree, pause time, and amount of time per day). Meta-analysis suggests that rotational therapy decreases the incidence of pneumonia but has no effect on duration of mechanical ventilation, number of days in intensive care, or hospital mortality. CONCLUSIONS: Rotational therapy may be useful for preventing and treating respiratory complications in selected critically ill patients receiving mechanical ventilation.

Beds↗
PubMed2006

Informed prognosis [corrected] after abdominal aortic aneurysm repair using predictive modeling techniques [corrected].

OBJECTIVE: To identify the best method for the prediction of postoperative mortality in individual abdominal aortic aneurysm surgery (AAA) patients by comparing statistical modelling with artificial neural networks' (ANN) and clinicians' estimates. METHODS: An observational multicenter study was conducted of prospectively collected postoperative Acute Physiology and Chronic Health Evaluation II data for a 9-year period from 24 intensive care units (ICU) in the Thames region of the United Kingdom. The study cohort consisted of 1205 elective and 546 emergency AAA patients. Four independent physiologic variables-age, acute physiology score, emergency operation, and chronic health evaluation-were used to develop multiple regression and ANN models to predict in-hospital mortality. The models were developed on 75% of the patient population and their validity tested on the remaining 25%. The results from these two models were compared with the observed outcome and clinicians' estimates by using measures of calibration, discrimination, and subgroup analysis. RESULTS: Observed in-hospital mortality for elective surgery was 9.3% (95% confidence interval [CI], 7.7% to 11.1%) and for emergency surgery, 46.7% (95% CI, 42.5 to 51.0%). The ANN and the statistical models were both more accurate than the clinicians' predictions. Only the statistical model was internally valid, however, when applied to the validation set of observations, as evidenced by calibration (Hosmer-Lemeshow C statistic, 14.97; P = .060), discrimination properties (area under receiver operating characteristic curve, 0.869; 95% CI, 0.824 to 0.913), and subgroup analysis. CONCLUSIONS: The prediction of in-hospital mortality in AAA patients by multiple regression is more accurate than clinicians' estimates or ANN modelling. Clinicians can use this statistical model as an objective adjunct to generate informed prognosis.

Aortic Aneurysm, Abdominal↗
PubMed2004

The longer patients are in hospital before Intensive Care admission the higher their mortality.

OBJECTIVE: To explore the relationship between hospital mortality and time spent by patients on hospital wards before admission to the intensive care unit (ICU). DESIGN: Observational study of prospectively collected data. SETTING: Participating intensive care units within the North East Thames Regional Database. PATIENTS AND PARTICIPANTS: Patients, 7,190, admitted to ICU from the hospital wards of 24 hospitals. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Of ICU admissions from the wards, 40.1% were in hospital for more than 3 days and 11.7% for more than 15 days. ICU patients who died in hospital were in-patients longer (p=0.001) before admission (median 3 days; interquartile range 1-9) than those discharged alive (median 2 days; interquartile range 1-5). Hospital mortality increased significantly (p<0.0001) in relation to time on hospital wards before ICU: 47.1% (standardised mortality ratio 1.09) for patients in hospital 0-3 days before ICU admission up to 67.2% (standardised mortality ratio 1.39) for patients on the wards for more than 15 days before ICU. Length of stay before ICU admission was an independent predictor of hospital mortality (odds ratio per day 1.019; 95% confidence interval 1.014-1.024). There were significant differences (p<0.001) in patient age, APACHE II score and predicted mortality in relation to time on wards before ICU admission. CONCLUSIONS: Mortality was high among patients admitted from the wards to ICU; many were inpatients for days or weeks before admission. The longer these patients were in hospital before ICU admission, the higher their mortality. Patients with delayed admission differed in some respects compared to those admitted earlier.

Adult↗
PubMed2004

Surgical mortality score: risk management tool for auditing surgical performance.

Existing methods of risk adjustment in surgical audit are complex and costly. The present study aimed to develop a simple risk stratification score for mortality and a robust audit tool using the existing resources of the hospital Patient Administration System (PAS) database. This was an observational study for all patients undergoing surgical procedures over a two-year period, at a London university hospital. Logistic regression analysis was used to determine predictive factors of in-hospital mortality, the study outcome. Odds ratios were used as weights in the derivation of a simple risk-stratification model-the Surgical Mortality Score (SMS). Observed-to-expected mortality risk ratios were calculated for application of the SMS model in surgical audit. There were 11,089 eligible cases, under five surgical specialties (maxillofacial, orthopedic, renal transplant/dialysis, general, and neurosurgery). Incomplete data were 3.7% of the total, with no evidence of systematic underreporting. The SMS model was well calibrated [Hosmer-Lemeshow C-statistic: development set (3.432, p = 0.33), validation set (6.359, p = 0.10) with a high discriminant ability (ROC areas: development set [0.837, S.E.=0.013] validation set [0.816, S.E. = 0.016]). Subgroup analyses confirmed that the model can be used by the individual specialties for both elective and emergency cases. The SMS is an accurate risk- stratification model derived from existing database resources. It is simple to apply as a risk-management, screening tool to detect aberrations from expected surgical outcomes and to assist in surgical audit.

Adult↗
PubMed2003

Intensive care admission decisions for a patient with limited survival prospects: a questionnaire and database analysis.

OBJECTIVE: To explore the concept of futility by asking clinicians for estimates of survival and admission decisions for an intensive care unit patient with little chance of survival, and to compare these estimates with results from an intensive care database. DESIGN: Questionnaire based on the presenting features of a genuine patient. It asked for estimated hospital survival, decision on intensive care admission, resuscitation status and importance of family views. Analysis of a regional intensive care database. SETTING: Physicians working in British intensive care units. PARTICIPANTS: We received 169 replies, 146 from consultants. MEASUREMENTS AND RESULTS: Median estimated hospital survival was 5%; 60% of consultants and 76% of trainees would have admitted the patient, with 9% and 14%, respectively, prepared to perform further cardiopulmonary resuscitation. Among those estimating survival probability as less than 1%, 17.2% would have admitted the patient. Family opinions were vital to 4.3% of respondents and unimportant to 9.8%. There were 251 patients in the database with similar physiological derangements. Their observed hospital mortality was 91%. At intensive care admission an admitting physician assessed 111 of these patients as 'expected to die'. Mortality in this group was 99.1% (one survivor). CONCLUSIONS: Experienced intensivists did not agree on estimated survival. Even when estimates agreed, admission decisions varied. Database analysis suggested that clinical judgement is relevant when assessing the risk of dying. Lack of consensus on survival estimates and admission decisions suggests that it would be difficult to achieve agreement on appropriate use of intensive care resources and on what constitutes futile treatment.

Critical Illness↗