Biomedical subjects

David L Tirschwell

Publications and source records attributed to David L Tirschwell.

3 recordsLinked to original sources

PubMed2003

Predictive value of somatosensory evoked potentials for awakening from coma.

OBJECTIVES: A systematic review of somatosensory evoked potentials performed early after onset of coma, to predict the likelihood of nonawakening. The pooled results were evaluated for rates of awakening, confidence intervals, and the possibility of rare exceptions. DATA SOURCES: Forty-one articles reporting somatosensory evoked potentials in comatose patients and subsequent outcomes, from 1983 to 2000. STUDY SELECTION: Studies were included if they reported coma etiology, age group, presence or absence of somatosensory evoked potentials, and coma outcomes. DATA EXTRACTION: We separated patients into four groups: adults with hypoxic-ischemic encephalopathy, adults with intracranial hemorrhage, adults and adolescents with traumatic brain injury, and children and adolescents with any etiologies. Somatosensory evoked potentials were categorized as normal, abnormal, or bilaterally absent. Outcomes were categorized as persistent vegetative state or death vs. awakening. DATA SYNTHESIS: For each somatosensory evoked potential result, rates of awakening (95% confidence interval) were calculated: adult hypoxic-ischemic encephalopathy: absent 0% (0%-1%), abnormal 22% (17%-26%), normal 52% (48%-56%); adult intracranial hemorrhage: absent 1% (0%-4%), present 38% (27%-48%); adult-teen traumatic brain injury: absent 5% (2%-7%), abnormal 70% (64%-75%), normal 89% (85%-92%); child-teen: absent 7% (4%-10%), abnormal 69% (61%-77%), normal 86% (80%-92%). CONCLUSIONS: Somatosensory evoked potential results predict the likelihood of nonawakening from coma with a high level of certainty. Adults in coma from hypoxic-ischemic encephalopathy with absent somatosensory evoked potential responses have <1% chance of awakening.

Adolescent↗

PubMed2002

Validating administrative data in stroke research.

BACKGROUND AND PURPOSE: Research based on administrative data has advantages, including large numbers, consistent data, and low cost. This study was designed to compare different methods of stroke classification using administrative data. METHODS: Administrative hospital discharge data and medical record review of 206 patients were used to evaluate 3 algorithms for classifying stroke patients. These algorithms were based on all (algorithm 1), the first 2 (algorithm 2), or the primary (algorithm 3) administrative discharge diagnosis code(s). The diagnoses after review of medical record data were considered the gold standard. Then, using a large administrative data set, we compared patients with a primary discharge diagnosis of stroke with patients with their stroke discharge diagnosis code in a nonprimary position. RESULTS: Compared with the gold standard, algorithm 1 had the highest kappa for classifying ischemic stroke, with a sensitivity of 86%, specificity of 95%, positive predictive value of 90%, and kappa=0.82. Algorithm 3 had the highest kappa values for intracerebral hemorrhage and subarachnoid hemorrhage. For intracerebral hemorrhage, the sensitivity was 85%, specificity was 96%, positive predictive value was 89%, and kappa=0.82. For subarachnoid hemorrhage, those values were 90%, 97%, 94%, and 0.88, respectively. Nonprimary position ischemic stroke patients had significantly greater comorbidity and 30-day mortality (odds ratio, 3.2) than primary position ischemic stroke patients. CONCLUSIONS: Stroke classification in these administrative data were optimal using all discharge diagnoses for ischemic stroke and primary discharge diagnosis only for intracerebral and subarachnoid hemorrhage. Selecting ischemic stroke patients on the basis of primary discharge diagnosis may bias administrative samples toward more benign, unrepresentative outcomes and should be avoided.

Algorithms↗

PubMed2002

Shortening the NIH Stroke scale for use in the prehospital setting.

BACKGROUND AND PURPOSE: Prehospital stroke scales should identify stroke patients and measure stroke severity. The goal of this study was to identify a subset of the 15 items in the National Institutes of Health Stroke Scale (NIHSS-15) that measures stroke severity and predicts outcomes. METHODS: Using 2 distinct data sets from acute stroke clinical trials, we derived and validated shortened versions of the NIHSS (sNIHSS). Stepwise logistic regression and bootstrap techniques were used in selection of NIHSS-15 items. Areas under the receiver operator characteristic curve (C statistics) were used to compare predictive performance of logistic models incorporating differing versions of the NIHSS. RESULTS: The derivation analyses suggested the 8 NIHSS-15 items that were most predictive of "good outcome" 3 months after stroke, in order of decreasing importance: right leg item, left leg, gaze, visual fields, language, level of consciousness, facial palsy, and dysarthria. The sNIHSS-8 comprises all 8 and the sNIHSS-5, the first 5. In the validation models, C statistics were NIHSS-15=0.80, sNIHSS-8=0.77, and sNIHSS-5=0.76. Statistical comparisons suggested that the NIHSS-15 had better predictive performance than the sNIHSS-8 or the sNIHSS-5; the absolute difference in C statistics was small. There was no significant difference between the sNIHSS-8 and the sNIHSS-5. CONCLUSIONS: Much of the predictive performance of the full NIHSS-15 was retained with a shortened scale, the sNIHSS-5. Shortening the NIHSS-15 will facilitate its use during prehospital evaluations. The sNIHSS severity information may be useful to triage acute stroke patients in communities and to provide a baseline stroke severity for prehospital acute stroke trials.

Acute Disease↗